Final Program
The Symposium on Simplicity in Algorithms (SOSA19) will take place on January 8 and 9.
SODA is jointly sponsored by the ACM Special Interest Group on Algorithms and Computation Theory, and the SIAM Activity Group on Discrete Mathematics
The SIAM Activity Group on Discrete Mathematics focuses on combinatorics, graph theory, cryptography, discrete optimization, mathematical programming, coding theory, information theory, game theory, and theoretical computer science, including algorithms, complexity, circuit design, robotics, and parallel processing. We provide an opportunity to unify pure discrete mathematics and areas of applied research such as computer science, operations research, combinatorics, and the social sciences.
3600 Market Street, 6th Floor Philadelphia, PA 19104-2688 USA Telephone: +1-215-382-9800 Fax: +1-215-386-7999 Conference E-mail: [email protected] Conference Web: siam.org/meetings Membership and Customer Service: (800) 447-7426 (USA & Canada) or +1-215-382-9800 (worldwide) https://www.siam.org/conferences/CM/Main/soda19 https://www.siam.org/conferences/CM/Main/alenex19 https://www.siam.org/conferences/CM/Main/analco19 https://simplicityinalgorithms.com/ 2 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Table of Contents Thore Husfeldt Shang-Hua Teng Lund University, Sweden and IT University University of Southern California, U.S. Program-at-a-Glance… of Copenhagen, Denmark ...... See separate handout Klaus Jansen ALENEX COMMITTEES General Information...... 2 Christian-Albrechts-Universität zu Kiel, Get-togethers...... 5 Germany Program Committee Best Paper and Best Student Paper ...... T.S. Jayram Co-Chairs Awards...... 10 IBM Almaden Research, U.S. Stephen Kobourov Invited Plenary Presentations...... 11 Daniel Kane University of Arizona, U.S. Program Schedule...... 12 University of California, San Diego, U.S. Henning Meyerhenke Speaker Index...... 32 Jonathan Kelner Humboldt-Universität zu Berlin, Germany Hotel Meeting Room Map...Back Cover Massachusetts Institute of Technology, U.S. Tsvi Kopelowitz Bar Ilan University, Israel Program Committee SODA COMMITTEES Pravesh K. Kothari Aaron Archer Program Committee Chair Princeton University and the Institute for Google, U.S. Advanced Study, U.S. Timothy M. Chan Maike Buchin Fabian Kuhn University of Illinois at Urbana-Champaign, Ruhr Universität Bochum, Germany University of Freiburg, Germany U.S. Aydin Buluc Daniel Lokshtanov Lawrence Berkeley National Laboratory, University of Bergen, Norway U.S. Program Committee Brendan Lucier Markus Chimani Peyman Afshani Microsoft Research, U.S. Osnabrück University, Germany Aarhus University, Denmark Bojan Mohar Pierluigi Crescenzi Shipra Agrawal Simon Fraser University, Canada Università degli Studi di Firenze, Italy Columbia University, U.S. Viswanath Nagarajan Yifan Hu Aditya Bhaskara University of Michigan, U.S. Yahoo!, U.S. University of Utah, U.S. Alantha Newman Yoichi Iwata Yang Cai CNRS, Grenoble, France National Institute of Informatics, Japan McGill University, Canada Ilan Newman Juha Kärkkäinen Amit Chakrabarti University of Haifa, Israel University of Helsinki, Finland Dartmouth College, U.S. Vijaya Ramachandran Ken-Ichi Kawarabayashi Deeparnab Chakrabarty University of Texas at Austin, U.S. National Institute of Informatics, Japan Dartmouth College, U.S. Ilya Razenshteyn Stefan Kratsch Erin Wolf Chambers Microsoft Research, U.S. Humboldt-Universität zu Berlin, Germany Saint Louis University, U.S. Liam Roditty Fredrik Manne Siu On Chan Bar Ilan University, Israel University of Bergen, Norway Chinese University of Hong Kong, Hong Kong Atri Rudra Nicole Megow Daniel Dadush University at Buffalo, SUNY, U.S. Universität Bremen, Germany CWI, Netherlands Laura Sanità Ulrich Meyer Holger Dell University of Waterloo, Canada Goethe University Frankfurt, Germany Saarland University, Germany László A. Végh Matthias Müller-Hannemann Zdenek Dvorak London School of Economics, United Martin-Luther-University Halle-Wittenberg, Charles University, Czech Republic Kingdom Germany Friedrich Eisenbrand Oren Weimann Matthias Petri EPFL, Switzerland University of Haifa, Israel The University of Melbourne, Australia Michael Elkin Udi Wieder Sergey Pupyrev Ben-Gurion University of the Negev, Israel VMware Research, U.S. Facebook, U.S. Jane Gao Barna Saha Monash University, Australia Steering Committee University of Massachusetts, Amherst, U.S. Sariel Har-Peled Renato Werneck University of Illinois at Urbana-Champaign, Pavol Hell Amazon, U.S. U.S. Simon Fraser University, Canada Hamed Hatami Daniel Král McGill University, Canada University of Warwick, United Kingdom Steering Committee Thomas Hayes Dana Randall Andrew V. Goldberg University of New Mexico, U.S. Georgia Institute of Technology, U.S. Amazon.com, U.S. (Chair) Martin Hoefer Cliff Stein Michael Goodrich Goethe University Frankfurt, Germany Columbia University, U.S. (chair) University of California, Irvine, U.S. Conference Program SODA, ALENEX, ANALCO and SOSA 2019 3
Giuseppe F. Italiano SODA Themes Hotel Address University of Rome “Tor Vergata”, Italy The Westin San Diego Rasmus Pagh Aspects of combinatorics and discrete IT University of Copenhagen, Denmark mathematics, such as: 400 West Broadway Vijaya Ramachandran Combinatorial structures San Diego, CA, 92101, U.S. University of Texas, Austin, U.S. Discrete optimization Cliff Stein Discrete probability Hotel Telephone Number Columbia University, U.S. Finite metric spaces Dorothea Wagner Graph theory To reach an attendee or leave a message, Karlsruhe Institute of Technology, Germany Mathematical programming call +1-619-239-4500. If the attendee Random structures is a hotel guest, the hotel operator can ANALCO COMMITTEES Topological problems connect you with the attendee’s room. Program Committee Core topics in discrete algorithms, Co-Chairs such as: Hotel Check-in and Marni Mishna Algorithm analysis Check-out Times Simon Fraser University, Canada Data structures Check-in time is 3:00 p.m. J. Ian Munro Experimental algorithmics Check-out time is 12:00 p.m. University of Waterloo, Canada Algorithmic aspects of other areas of computer science, such as: Child Care Program Committee Combinatorial scientific computing California DMC recommends Martin Dietzfelbinger Communication networks and the Destinations Sitters (https://www. Technische Universität Ilmenau, Germany Internet destinationsitters.com/) for attendees Cecilia Holmgren Computational geometry and topology interested in child care services. Uppsala University, Sweden Computer graphics and computer vision Attendees are responsible for making Mihyun Kang Computer systems their own child care arrangements. Technische Universität Graz, Austria Cryptography and security Yusuke Kobayashi Databases and information retrieval Kyoto University, Japan Data compression Corporate/Institutional Jérémie Lumbroso Data privacy Members Princeton University, U.S. Distributed and parallel computing Hosam Mahmoud SIAM corporate members provide George Washington University, U.S. Game theory and mechanism design their employees with knowledge about, Daniel Panario Machine learning access to, and contacts in the applied Carleton University, Canada Quantum computing mathematics and computational sciences Dominique Poulalhon community through their membership Université Paris Diderot, France SIAM Registration Desk benefits. Corporate membership is more Sebastian Wild than just a bundle of tangible products University of Waterloo, Canada The registration desk is on the 2nd Floor. It is open during the following hours: and services; it is an expression of support for SIAM and its programs. Steering Committee Saturday, January 5 SIAM is pleased to acknowledge its 5:00 p.m. – 8:00 p.m. corporate members and sponsors. In Michael Drmota Technische Universität Wien, Austria recognition of their support, non-member Sunday, January 6 James Allen Fill (Jim Fill) attendees who are employed by the Johns Hopkins University, U.S. 8:00 a.m. – 5:00 p.m. following organizations are entitled to the H.K. Hwang SIAM member registration rate. Institute of Statistical Science, Academia Monday, January 7 Sinica, Taiwan 8:00 a.m. – 5:00 p.m. Conrado Martínez Corporate/Institutional Universitat Politècnica de Catalunya, Spain Tuesday, January 8 Members Markus Nebel 8:00 a.m. – 5:00 p.m. Universität Bielefeld, Germany The Aerospace Corporation Robert Sedgewick Wednesday, January 9 Air Force Office of Scientific Research Princeton University, U.S. 8:00 a.m. – 5:00 p.m. Wojciech Szpankowski Amazon Purdue University, U.S. Argonne National Laboratory Mark Daniel Ward Purdue University, U.S. Bechtel Marine Propulsion Laboratory 4 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
The Boeing Company subscriptions to SIAM Review, SIAM connections. Presenters requiring an CEA/DAM News and SIAM Unwrapped, and enjoy alternate connection must provide their substantial discounts on SIAM books, own adaptor. Cirrus Logic journal subscriptions, and conference Department of National Defence (DND/ registrations. CSEC) Internet Access If you are not a SIAM member and did Attendees booked within the SIAM room DSTO- Defence Science and not join SIAM before you registered, you block will receive complimentary wireless Technology Organisation, Edinburgh can apply the difference of $140 between internet access in their guest rooms and the Exxon Mobil what you paid as a non-member, and public areas of the hotel. All conference what a member paid towards a SIAM IDA Center for Communications attendees will have complimentary wireless membership. Contact SIAM Customer Research, La Jolla internet access in the meeting space of the Service for details or join at the hotel. IDA Institute for Defense Analyses, conference registration desk. Princeton SIAM will provide a limited number If you are a SIAM member, it only costs of email stations for attendees during IDA Institute for Defense Analyses, $15 to join the SIAM Activity Group on registration hours. Bowie, Maryland Discrete Mathematics. Lawrence Berkeley National Laboratory Students who paid the Student Non Registration Fee Includes Lawrence Livermore National Labs Member Rate will be automatically enrolled as SIAM Student Members. • Admission to all technical sessions Lockheed Martin Maritime Systems & Please go to my.siam.org to update (SODA19, ALENEX19, ANALCO19, Sensors your education and contact information SOSA19) Los Alamos National Laboratory in your profile. If you attend a SIAM • ANALCO/ALENEX and SOSA Business meetings Max-Planck-Institute Academic Member Institution or are part of a SIAM Student Chapter you will be • Coffee breaks daily Mentor Graphics able to renew next year for free. • Continental Breakfast daily National Institute of Standards and Join onsite at the registration desk, go • Luncheon on Sunday, January 6 Technology (NIST) to https://www.siam.org/Membership/ • Proceedings (SODA, ALENEX, and National Security Agency Join-SIAM to join online or download ANALCO posted online) an application form, or contact SIAM Oak Ridge National Laboratory • Room set-ups and audio/visual Customer Service: equipment Sandia National Laboratories Telephone: +1-215-382-9800 (worldwide); • SODA Business Meeting Schlumberger or 800-447-7426 (U.S. and Canada only) Fax: +1-215-386-7999 • Welcome Reception Simons Foundation Email: [email protected] United States Department of Energy Postal mail: Society for Industrial and Job Postings Applied Mathematics, 3600 Market U.S. Army Corps of Engineers, Please check with the SIAM registration Engineer Research and Development Street, 6th floor, Philadelphia, PA 19104-2688 U.S. desk regarding the availability of job Center postings or visit https://jobs.siam.org/. List current November 2018. Standard Audio-Visual Set-Up in Meeting Rooms Table Top Display Join SIAM and save! SIAM does not provide computers for Cambridge University Press Leading the applied any speaker. When giving an electronic mathematics community . . . presentation, speakers must provide their own computers. SIAM is not responsible SIAM members save up to $140 for the safety and security of speakers’ on full registration for SODA and computers. its associated meetings. Join your peers in supporting the premier A data (LCD) projector and screen will professional society for applied be provided in all technical session mathematicians and computational meeting rooms. The data projectors scientists. SIAM members receive support both VGA and HDMI Conference Program SODA, ALENEX, ANALCO and SOSA 2019 5
Conference Sponsors Statement on Inclusiveness Recording of Presentations As a professional society, SIAM is Audio and video recording of presenta- committed to providing an inclusive tions at SIAM meetings is prohibited climate that encourages the open without the written permission of the expression and exchange of ideas, that presenter and SIAM. is free from all forms of discrimination, 2019 Conference Bag Sponsor harassment, and retaliation, and that Social Media is welcoming and comfortable to all SIAM is promoting the use of members and to those who participate social media, such as Facebook and in its activities. In pursuit of that Twitter, in order to enhance scientific commitment, SIAM is dedicated to the discussion at its meetings and enable philosophy of equality of opportunity and attendees to connect with each other Name Badges treatment for all participants regardless prior to, during and after conferences. A space for emergency contact of gender, gender identity or expression, If you are tweeting about a conference, information is provided on the back of sexual orientation, race, color, national please use the designated hashtag your name badge. Help us help you in the or ethnic origin, religion or religious to enable other attendees to keep event of an emergency! belief, age, marital status, disabilities, up with the Twitter conversation veteran status, field of expertise, or any and to allow better archiving of our other reason not related to scientific merit. Comments? conference discussions. The hashtags This philosophy extends from SIAM for these meetings are #SIAMDA19, Comments about SIAM meetings are conferences, to its publications, and #ANALCO19 and #ALENEX19. encouraged! Please send to: to its governing structures and bodies. Cynthia Phillips, SIAM Vice President for We expect all members of SIAM and SIAM’s Twitter handle is Programs ([email protected]). participants in SIAM activities to work @TheSIAMNews. towards this commitment.is welcoming Get-togethers and comfortable to all members and to those who participate in its activities. Changes to the Printed Welcome Reception In pursuit of that commitment, SIAM is Program Saturday, January 5 dedicated to the philosophy of equality The printed program was current at 6:00 p.m. – 8:00 p.m. of opportunity and treatment for all the time of printing, however, please Poolside and Ivory - 3rd Floor participants regardless of gender, gender review the online program schedule ALENEX and ANALCO Business identity or expression, sexual orientation, (http://meetings.siam.org/program. Meeting race, color, national or ethnic origin, cfm?CONFCODE=da19) for up-to- Sunday, January 6 religion or religious belief, age, marital date information. 6:45 p.m. – 7:45 p.m. status, disabilities, veteran status, field Diamond 2 - 2nd Floor of expertise, or any other reason not related to scientific merit. This philosophy SODA Business Meeting and Awards extends from SIAM conferences, to Presentation its publications, and to its governing Monday, January 7 structures and bodies. We expect all 6:45 p.m. – 7:45 p.m. members of SIAM and participants in Emerald Ballroom - 2nd Floor SIAM activities to work towards this Complimentary beer and wine will be commitment. served. SOSA Business Meeting Tuesday, January 8 Please Note 6:45 p.m. – 7:45 p.m. SIAM is not responsible for the safety and Diamond 2 - 2nd Floor security of attendees’ computers. Do not leave your laptop computers unattended. Please remember to turn off your cell phones, pagers, etc. during sessions. 6 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program Conference Program SODA, ALENEX, ANALCO and SOSA 2019 7 8 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program Conference Program SODA, ALENEX, ANALCO and SOSA 2019 9 10 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Best Paper and Best Student Paper Awards
Best Paper Award Sublinear Algorithms for (∆ + 1) Vertex Coloring Sepehr Assadi, Yu Chen, and Sanjeev Khanna, University of Pennsylvania, U.S. This paper will be presented on Monday, January 7, in session CP14 SODA Session 4A. See page 18 for session details.
Best Student Paper Award
Optimal Las Vegas Approximate Near Neighbors in ℓp Alexander Wei, Harvard University, U.S. This paper will be presented on Tuesday, January 8, in session CP30 SODA Session 8A. See page 25 for session details. Conference Program SODA, ALENEX, ANALCO and SOSA 2019 11
Invited Plenary Speakers All Invited Plenary Presentations will take place in Emerald Ballroom - 2nd Floor
Sunday, January 6 11:30 a.m. - 12:30 p.m IP1 Towards Analysis of Information Content in Dynamic Networks Wojciech Szpankowski, Purdue University, U.S.
Monday, January 7 11:30 a.m. - 12:30 p.m. IP2 Recent Advances on the Complexity of Parameterized Counting Problems Dániel Marx, Hungarian Academy of Sciences, Hungary
Tuesday, January 8 11:30 a.m. - 12:30 p.m. IP3 Inherent Trade-Offs in Algorithmic Fairness Jon M. Kleinberg, Cornell University, U.S.
Wednesday, January 9 11:30 a.m. - 12:30 p.m. IP4 (Hardness of) Approximation and Expansion Irit Dinur, Weizmann Institute of Science, Israel 12 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Program Schedule
Symposium on Simplicity in Algorithms (SOSA19) Conference Program SODA, ALENEX, ANALCO and SOSA 2019 13
Sunday, January 6 Saturday, January 5 Sunday, January 6 CP1 ANALCO Session 1 Registration Registration 9:00 AM-11:05 AM 5:00 PM-8:00 PM 8:00 AM-5:00 PM Room: Diamond 2 - 2nd Floor Room: Registration Desk - 2rd Floor Room: Registration Desk - 2rd Floor Chair: Sebastian Wild, University of Waterloo, Canada 9:00-9:20 Combinatorics of Welcome Reception Continental Breakfast Nondeterministic Walks of the Dyck and Motzkin Type 6:00 PM-8:00 PM 8:30 AM-9:00 AM Elie de Panafieu, Nokia Bell Labs France, Room: Poolside and Ivory - 3rd Floor Room: Ballroom Foyer - 2nd Floor France; Mohamed Lamine Lamali and Michael Wallner, LaBRI - Université de Bordeaux, France 9:25-9:45 Ranked Schröder Trees Antoine Genitrini, Sorbonne Universités, France; Olivier Bodini, Université Paris 13, France; Mehdi Naima, Universite Paris- Nord, France 9:50-10:10 Esthetic Numbers and Lifting Restrictions on the Analysis of Summatory Functions of Regular Sequences Clemens Heuberger and Daniel Krenn, Alpen-Adria-Universität Klagenfurt, Austria 10:15-10:35 Reducing Simply Generated Trees by Iterative Leaf Cutting Benjamin Hackl and Clemens Heuberger, Alpen-Adria-Universität Klagenfurt, Austria; Stephan Wagner, Stellenbosch University, South Africa 10:40-11:00 Protection Number of Recursive Trees Zbigniew Golebiewski and Mateusz Klimczak, Wroclaw University of Science and Technology, Poland 14 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Sunday, January 6 Sunday, January 6 Sunday, January 6 CP2 CP3 CP4 SODA Session 1A SODA Session 1B SODA Session 1C 9:00 AM-11:05 AM 9:00 AM-11:05 AM 9:00 AM-11:05 AM Room: Emerald Ballroom - 2nd Floor Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: To Be Determined 9:00-9:20 Fine-grained Complexity 9:00-9:20 Metrical Task Systems on 9:00-9:20 Dynamic Double Auctions: Meets IP = PSPACE Trees Via Mirror Descent and Unfair Towards First Best Lijie Chen and Shafi Goldwasser, Gluing Santiago Balseiro, Columbia University, Massachusetts Institute of Technology, Sebastien Bubeck, Microsoft Research, U.S.; Vahab Mirrokni, Google, Inc., U.S.; U.S.; Kaifeng Lyu, Tsinghua University, U.S.; Michael B. Cohen, Massachusetts Renato Paes Leme and Song Zuo, Google China; Guy Rothblum, Microsoft Research, Institute of Technology, U.S.; James Research, U.S. U.S.; Aviad Rubinstein, Harvard University, R. Lee and Yin Tat Lee, University of 9:25-9:45 Multi-unit Supply-monotone Washington, U.S. U.S. Auctions with Bayesian Valuations 9:25-9:45 An Equivalence Class for 9:25-9:45 K-Servers with a Smile: Yuan Deng and Debmalya Panigrahi, Duke Orthogonal Vectors Online Algorithms via Projections University, U.S. Lijie Chen and Ryan Williams, Massachusetts Marco Molinaro, Pontifical Catholic 9:50-10:10 Correlation-robust Analysis University of Rio de Janeiro, Brazil; Institute of Technology, U.S. of Single Item Auction Niv Buchbinder, Tel Aviv University, 9:50-10:10 Seth-Based Lower Bounds Xiaohui Bei, Nanyang Technological Israel; Anupam Gupta, Carnegie Mellon for Subset Sum and Bicriteria Path University, Singapore; Nick Gravin and University, U.S.; Joseph Naor, Technion Amir Abboud, IBM Almaden Research Pinyan Lu, Shanghai University of Finance Israel Institute of Technology, Israel Center, U.S.; Karl Bringmann, Max Planck and Economics, China; Zhihao Gavin Tang, Institute for Informatics, Germany; Danny 9:50-10:10 A Nearly-Linear Bound for University of Hong Kong, Hong Kong Chasing Nested Convex Bodies Hermelin and Dvir Shabtay, Ben-Gurion 10:15-10:35 Tight Revenue Gaps C.J. Argue, Carnegie Mellon University, University, Israel among Simple Mechanisms U.S.; Sebastien Bubeck, Microsoft Yaonan Jin, Hong Kong University of Science 10:15-10:35 Fast Modular Subset Sum Research, U.S.; Michael B. Cohen, and Technology, Hong Kong; Pinyan Using Linear Sketching Massachusetts Institute of Technology, Lu, Shanghai University of Finance and Kyriakos Axiotis, Massachusetts Institute U.S.; Anupam Gupta, Carnegie Mellon Economics, China; Zhihao Gavin Tang, of Technology, U.S.; Arturs Backurs, University, U.S.; Yin Tat Lee, University University of Hong Kong, Hong Kong; Tao Toyota Technological Institute at Chicago, of Washington, U.S. Xiao, Shanghai Jiao Tong University, China U.S.; Ce Jin, Tsinghua University, China; 10:15-10:35 A Ø-Competitive Christos Tzamos, University of Wisconsin, Algorithm for Scheduling Packets 10:40-11:00 Assignment Mechanisms Madison, U.S.; Hongxun Wu, Tsinghua with Deadlines under Distributional Constraints University, China Pavel Vesely, University of Warwick, United Itai Ashlagi, Amin Saberi, and Ali Shameli, 10:40-11:00 A Subquadratic Kingdom; Marek Chrobak, University of Stanford University, U.S. Approximation Scheme for Partition California, Riverside, U.S.; Lukasz Jez, Marcin Mucha, Karol Wegrzycki, and Michal Tel Aviv University, Israel and University Wlodarczyk, University of Warsaw, Poland of Wroclaw, Poland; Jiri Sgall, Charles Coffee Break University, Czech Republic 11:05 AM-11:30 AM 10:40-11:00 Elastic Caching Debmalya Panigrahi, Duke University, Room: Ballroom Foyer - 2nd Floor U.S.; Anupam Gupta, Carnegie Mellon University, U.S.; Ravishankar Krishnaswamy, Microsoft Research, India; Amit Kumar, Indian Institute of Technology, Delhi, India Conference Program SODA, ALENEX, ANALCO and SOSA 2019 15
Sunday, January 6 Sunday, January 6 Sunday, January 6 IP1 CP5 CP6 Towards Analysis of ANALCO Session 2 SODA Session 2A Information Content in 2:00 PM-3:40 PM 2:00 PM-4:05 PM Dynamic Networks Room: Diamond 2 - 2nd Floor Room: Emerald Ballroom - 2nd Floor 11:30 AM-12:30 PM Chair: Hosam Mahmoud, George Washington Chair: To Be Determined Room: Emerald Ballroom - 2nd Floor University, U.S. 2:00-2:20 Deterministic (1/2 + Chair: To Be Determined 2:00-2:20 Sesquickselect: One and ε)-Approximation for Submodular a Half Pivots for Cache-Efficient Maximization over a Matroid Shannon information theory has Selection Niv Buchbinder, Tel Aviv University, Israel; served as a bedrock for advances in Sebastian Wild, University of Waterloo, Moran Feldman and Mohit Garg, Open communication and storage systems Canada; Conrado Martínez, Universidad University of Israel, Israel over the past six decades. However, Politecnica de Catalunya, Spain; Markus 2:25-2:45 Submodular Maximization this theory does not handle well Nebel, Universität Bielefeld, Germany with Nearly Optimal Approximation, higher order structures (e.g., graphs, 2:25-2:45 Moments of Select Sets Adaptivity and Query Complexity geometric structures), temporal aspects Simon H. Langowski and Mark Daniel Ward, Matthew Fahrbach, Georgia Institute of (e.g., real-time considerations), or Purdue University, U.S. Technology, U.S.; Vahab Mirrokni and semantics, which are essential aspects Morteza Zadimoghaddam, Google, Inc., 2:50-3:10 Median-of-K Jumplists and of data and information that underlie U.S. Dangling-Min BSTs a broad class of current and emerging Sebastian Wild, University of Waterloo, 2:25-2:45 Submodular Maximization data science applications. In this talk, Canada; Markus Nebel, Universität with Nearly-optimal Approximation we present some recent results on Bielefeld, Germany; Elisabeth Neumann, and Adaptivity in Nearly-linear Time structural and temporal information in Technische Universität Braunschweig, Alina Ene, Boston University, U.S.; Huy dynamic networks/graphs, in which Germany Nguyen, Northeastern University, U.S. nodes and edges are added and removed 3:15-3:35 QuickSort: Improved Right- 2:25-2:45 An Exponential Speedup in over time. We focus on two related Tail Asymptotics for the Limiting Parallel Running Time for Submodular problems: (i) compression of structures Distribution, and Large Deviations Maximization Without Loss in -- for a given graph model, we exhibit James Allen Fill and Wei-Chun Hung, Johns Approximation an efficient algorithm for invertibly Hopkins University, U.S. Eric Balkanski, Aviad Rubinstein, and Yaron mapping network structures (i.e., graph Singer, Harvard University, U.S. isomorphism types) to bit strings of 2:50-3:10 Submodular Function minimum expected length, and (ii) node Maximization in Parallel via the arrival order inference -- for a dynamic Multilinear Extension graph model, we determine the extent Chandra Chekuri and Kent Quanrud, to which the order of node arrivals can University of Illinois at Urbana- be inferred from a snapshot of the graph Champaign, U.S. structure. For both problems, we apply 3:15-3:35 Stochastic Submodular analytic combinatorics, probabilistic, and Cover with Limited Adaptivity information-theoretic methods to find Arpit Agarwal, Sepehr Assadi, and Sanjeev statistical limits and efficient algorithms Khanna, University of Pennsylvania, U.S. for achieving those limits. 3:40-4:00 Stochastic ℓp Load Balancing and Moment Problems via Wojciech Szpankowski the L-Function Method Purdue University, U.S. Marco Molinaro, Pontifical Catholic University of Rio de Janeiro, Brazil Luncheon **Ticketed Event** 12:30 PM-2:00 PM Room:Crystal Ballroom - 2nd Floor
Please visit the SIAM Registration Desk if you did not receive a luncheon ticket. 16 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Sunday, January 6 Sunday, January 6 Sunday, January 6 CP7 CP8 CP9 SODA Session 2B SODA Session 2C ANALCO Session 3 2:00 PM-4:05 PM 2:00 PM-4:05 PM 4:30 PM-6:35 PM Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Room: Diamond 2 - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: Markus Nebel, Universität Bielefeld, 2:00-2:20 Approximate Nearest 2:00-2:20 An Algorithmic Blend of LPs Germany Neighbor Searching with Non- and Ring Equations for Promise CSPs 4:30-4:50 When Does Hillclimbing Fail euclidean and Weighted Distances Joshua Brakensiek, Stanford University, U.S.; on Monotone Functions? Ahmed Abdelkader, University of Maryland, Venkatesan Guruswami, Carnegie Mellon Anders Martinsson, ETH Zürich, Switzerland U.S.; Sunil Arya, Hong Kong University University, U.S. 4:55-5:15 Degree Distributions of of Science and Technology, Hong Kong; 2:25-2:45 The Complexity of the Ideal Generalized Hooking Networks Guilherme D. da Fonseca, Université Membership Problem for Constrained Colin Desmarais and Cecilia Holmgren, Clermont Auvergne, France; David M. Problems Over the Boolean Domain Uppsala University, Sweden Mount, University of Maryland, U.S. Monaldo Mastrolilli, IDSIA, Switzerland 5:20-5:40 Subcritical Random 2:25-2:45 Colored Range Closest- 2:50-3:10 Perfect Matchings, Rank Hypergraphs, High-Order Pair Problem under General Distance of Connection Tensors and Graph Components, and Hypertrees Functions Homomorphisms Wenjie Fang, Oliver Cooley, Nicola Del Jie Xue, University of Minnesota, Twin Cities, Jin-Yi Cai and Artsiom Hovarau, University Giudice, and Mihyun Kang, Technische U.S. of Wisconsin, Madison, U.S. Universität, Graz, Austria 2:50-3:10 Optimal Algorithm for 3:15-3:35 Probabilistic Tensors 5:45-6:05 Random Walks on Graphs: Geodesic Nearest-point Voronoi and Opportunistic Boolean Matrix New Bounds on Hitting, Meeting, Diagrams in Simple Polygons Multiplication Coalescing and Returning Eunjin Oh, Max Planck Institute for Petteri Kaski and Matti Karppa, Aalto Roberto Oliveira, IMPA, Rio de Janeiro, Informatics, Germany University, Finland Brazil; Yuval Peres, Microsoft Research, 3:15-3:35 New Lower Bounds for the 3:40-4:00 Fast Greedy for Linear U.S. Number of Pseudoline Arrangements Matroids Adrian Dumitrescu and Ritankar Mandal, 6:10-6:30 Arithmetic Progression Huy L. Nguyen, Northeastern University, U.S. University of Wisconsin, Milwaukee, U.S. Hypergraphs: Examining the Second Moment Method 3:40-4:00 Extremal and Probabilistic Michael Mitzenmacher, Harvard University, Results for Order Types Coffee Break U.S. Jie Han, University of Rhode Island, U.S.; Yoshiharu Kohayakawa, Universidade 4:05 PM-4:30 PM de Sao Paulo, Brazil; Marcelo Sales, Room:Ballroom Foyer - 2nd Floor Emory University, U.S.; Henrique Stagni, Universidade of São Paulo, Brazil Conference Program SODA, ALENEX, ANALCO and SOSA 2019 17
Sunday, January 6 Sunday, January 6 Sunday, January 6 CP10 CP11 CP12 SODA Session 3A SODA Session 3B SODA Session 3C 4:30 PM-6:35 PM 4:30 PM-6:35 PM 4:30 PM-6:35 PM Room: Emerald Ballroom - 2nd Floor Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: To Be Determined 4:30-4:50 Flow-Cut Gaps and Face 4:30-4:50 Reproducibility and Pseudo- 4:30-4:50 Anaconda: A Non-adaptive Covers in Planar Graphs determinism in Log-space Conditional Sampling Algorithm for Havana Rika and Robert Krauthgamer, Yang Liu, Stanford University, U.S.; Ofer Distribution Testing Weizmann Institute of Science, Israel; Grossman, University of California, Gautam Kamath, Simons Institute for the James R. Lee, University of Washington, Berkeley, U.S. Theory of Computing, U.S.; Christos U.S. 4:55-5:15 Pseudorandomness for Tzamos, University of Wisconsin, Madison, 4:55-5:15 On Constant Multi- Read-k DNF Formulas U.S. Commodity Flow-Cut Gaps for Rocco A. Servedio, Columbia University, 4:55-5:15 Testing Halfspaces over Families of Directed Minor-Free U.S.; Li-Yang Tan, Stanford University, Rotation-invariant Distributions Graphs U.S. Nathaniel Harms, University of Waterloo, Ario Salmasi, Ohio State University, U.S.; 5:20-5:40 Near-optimal Bootstrapping Canada Anastasios Sidiropoulos, University of of Hitting Sets for Algebraic Circuits 5:20-5:40 Every Testable (Infinite) Illinois at Chicago, U.S.; Vijay Sridhar, Mrinal Kumar, Simons Institute for the Property of Bounded-degree Graphs MathWorks, U.S. Theory of Computing, U.S.; Ramprasad Contains An Infinite Hyperfinite 5:20-5:40 Maximum Integer Flows in Saptharishi and Anamay Tengse, Tata Subproperty Directed Planar Graphs with Vertex Institute of Fundamental Research, India Hendrik Fichtenberger, Technische Capacities and Multiple Sources and 5:45-6:05 A Deterministic PTAS for the Universität Dortmund, Germany; Pan Peng, Sinks Algebraic Rank of Bounded Degree University of Sheffield, United Kingdom; Yipu Wang, University of Illinois at Urbana- Polynomials Christian Sohler, Technische Universität Champaign, U.S. Vishwas Bhargava, Rutgers University, Dortmund, Germany 5:45-6:05 A Faster Algorithm for U.S.; Markus Bläser, Saarland University, 5:45-6:05 Testing Matrix Rank, Minimum-Cost Bipartite Matching in Germany; Gorav Jindal, Aalto University, Optimally Minor-Free Graphs Finland; Anurag Pandey, Max Planck Maria-Florina Balcan, Carnegie Mellon Nathaniel Lahn and Sharath Raghvendra, Institute for Informatics, Germany University, U.S.; Yi Li, Nanyang Virginia Tech, U.S. 6:10-6:30 Quantum Algorithms and Technological University, Singapore; David Woodruff and Hongyang Zhang, 6:10-6:30 Finding Maximal Sets of Approximating Polynomials for Laminar 3-Separators in Planar Graphs Composed Functions with Shared Carnegie Mellon University, U.S. Inputs in Linear Time 6:10-6:30 A Ptas for ℓp-Low Rank David Eppstein, University of California, Mark Bun, Simons Institute for the Theory of Approximation Irvine, U.S. Computing, U.S.; Robin Kothari, Microsoft Frank Ban, University of California, Research, U.S.; Justin Thaler, Georgetown Berkeley, U.S.; Vijay Bhattiprolu, Carnegie University, U.S. Mellon University, U.S.; Karl Bringmann and Pavel Kolev, Max Planck Institute for Informatics, Germany; Euiwoong Lee, New York University, U.S.; David Woodruff, Carnegie Mellon University, U.S.
Intermission 6:35 PM-6:45 PM
ALENEX/ANALCO Business Meeting 6:45 PM-7:45 PM Room: Diamond 2 - 2nd Floor 18 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Monday, January 7 Monday, January 7 Monday, January 7 CP13 CP14 ALENEX Session 1 SODA Session 4A Registration 9:00 AM-10:40 AM 9:00 AM-11:05 AM 8:00 AM-5:00 PM Room: Diamond 2 - 2nd Floor Room: Emerald Ballroom - 2nd Floor Room: Registration Desk - 2rd Floor Chair: Henning Meyerhenke, Humboldt- Chair: To Be Determined Universität zu Berlin, Germany 9:00-9:20 Sublinear Algorithms for (∆ + 1) 9:00-9:20 Worst-case Efficient Sorting Vertex Coloring Continental Breakfast with QuickMergesort Sepehr Assadi, Yu Chen, and Sanjeev Khanna, 8:30 AM-9:00 AM Armin Weiß, Universität Stuttgart, Germany; University of Pennsylvania, U.S. Stefan Edelkamp, King’s College London, Room:Ballroom Foyer - 2nd Floor 9:25-9:45 Optimal Distributed Coloring United Kingdom Algorithms for Planar Graphs in the 9:25-9:45 Simple and Fast Local Model BlockQuicksort using Lomuto’s Shiri Chechik and Doron Mukhtar, Tel Aviv Partitioning Scheme University, Israel Martin Aumüller and Nikolaj Hass, IT 9:50-10:10 Distributed Maximal University of Copenhagen, Denmark Independent Set Using Small 9:50-10:10 Lightweight Distributed Messagesu Suffix Array Construction Mohsen Ghaffari, ETH Zürich, Switzerland Florian Kurpicz and Johannes Fischer, 10:15-10:35 Distributed Triangle Technische Universität Dortmund, Detection via Expander Germany Decomposition 10:15-10:35 Approximation of Trees by Yi-Jun Chang and Seth Pettie, University of Self-nested Trees Michigan, U.S.; Hengjie Zhang, Tsinghua Romain Azais, Inria, France; Jean-Baptiste University, China Durand, Université Grenoble Alpes, 10:40-11:00 Oblivious Resampling France; Christophe Godin, Inria, France Oracles and Parallel Algorithms for the Lopsided Lovász Local Lemma David G. Harris, University of Maryland, U.S. Conference Program SODA, ALENEX, ANALCO and SOSA 2019 19
Monday, January 7 Monday, January 7 Monday, January 7 CP15 CP16 IP2 SODA Session 4B SODA Session 4C Recent Advances on the 9:00 AM-11:05 AM 9:00 AM-11:05 AM Complexity of Parameterized Counting Problems Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Chair: To Be Determined Chair: To Be Determined 11:30 AM-12:30 PM 9:00-9:20 On the Number of Circuits in 9:00-9:20 On the Rank of a Random Room: Emerald Ballroom - 2nd Floor Regular Matroids (with Connections to Sparse Binary Matrix Chair: To Be Determined Lattices and Codes) Alan Frieze, Carnegie Mellon University, Rohit Gurjar, Indian Institute of Technology U.S.; Colin Cooper, King’s College Research in the past few decades revealed Bombay, India; Nisheeth K. Vishnoi, École London, United Kingdom; Wesley Pegden, that a many of the basic algorithmic Polytechnique Fédérale de Lausanne, Carnegie Mellon University, U.S. problems are fixed-parameter tractable Switzerland with various parameterizations: they 9:25-9:45 On Coalescence Time in can be solved in time f(k)n^c, where k 9:25-9:45 Minimum Cut and Minimum Graphs-When Is Coalescing as Fast k-Cut in Hypergraphs via Branching as Meeting? is some parameter of the input instance. Contractions Varun Kanade, University of Oxford, United There have been significant progress Kyle Fox, University of Texas, Dallas, U.S.; Kingdom; Frederik Mallmann-Trenn, both in the design of parameterized Debmalya Panigrahi, Duke University, Massachusetts Institute of Technology, algorithms and in understanding the U.S.; Fred Zhang, Harvard University, U.S. U.S.; Thomas Sauerwald, University of limitations of these techniques. However, 9:50-10:10 Improving the Smoothed Cambridge, United Kingdom up until recent years, there were relatively Complexity of Flip for Max-cut 9:50-10:10 Rapid Mixing of the Switch few algorithmic and complexity results Problems Markov Chain for Strongly Stable on parameterized counting problems. Ali Bibak, University of Illinois at Chicago, Degree Sequences and 2-Class Joint Similarly to what one experiences in the U.S.; Charles A. Carlson, University of Degree Matrices area of polynomial-time computations, Colorado Boulder, U.S.; Karthekeyan Georgios Amanatidis and Pieter Kleer, there are natural parameterized problems Chandrasekaran, University of Illinois at Centrum voor Wiskunde en Informatica where finding a single solution is fixed- Urbana-Champaign, U.S. (CWI), Netherlands parameter tractable, but counting the 10:15-10:35 Computing all Wardrop 10:15-10:35 Xor Codes and Learning number of solutions is hard. In this talk, I Equilibria Parametrized by the Flow Sparse Parities with Noise will give a survey of recent developments, Demand Andrej Bogdanov, Chinese University of including new results that give a clean Philipp Warode and Max Klimm, Humboldt Hong Kong, Hong Kong; Manuel Sabin and unified explanation for the complexity and Prashant Nalini Vasudevan, University University at Berlin, Germany of #k-Path, #k-Matching, and many other of California, Berkeley, U.S. 10:40-11:00 Nash Flows over Time with parameterized counting problems. Spillback 10:40-11:00 Seeded Graph Matching Leon Sering, Technische Universität Berlin, via Large Neighborhood Statistics Dániel Marx Germany; Laura Vargas Koch, RWTH Elchanan Mossel, Massachusetts Institute Hungarian Academy of Sciences, Hungary Aachen University, Germany of Technology, U.S.; Jiaming Xu, Duke University, U.S. Lunch Break Coffee Break 12:30 PM-2:00 PM 11:05 AM-11:30 AM Attendees on their own Room: Ballroom Foyer - 2nd Floor 20 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Monday, January 7 Monday, January 7 Monday, January 7 CP17 CP18 CP19 ALENEX Session 2 SODA Session 5A SODA Session 5B 2:00 PM-3:40 PM 2:00 PM-4:05 PM 2:00 PM-4:05 PM Room: Diamond 2 - 2nd Floor Room: Emerald Ballroom - 2nd Floor Room: Topaz - 2nd Floor Chair: Andrew Goldberg, Amazon.com, U.S. Chair: To Be Determined Chair: To Be Determined 2:00-2:20 Fast and Exact Public Transit 2:00-2:20 Nearly ETH-tight Algorithms 2:00-2:20 The Streaming k-Mismatch Routing with Restricted Pareto Sets for Planar Steiner Tree with Terminals Problem Daniel Delling, Julian Dibbelt, and Thomas on Few Faces Raphael Clifford, University of Bristol, Pajor, Apple, Inc., U.S. Sándor Kisfaludi-Bak and Jesper Nederlof, United Kingdom; Tomasz Kociumaka, Eindhoven University of Technology, 2:25-2:45 Alternative Multicriteria University of Warsaw, Poland; Ely Porat, Netherlands; Erik Jan van Leeuwen, Routes Bar-Ilan University, Israel Utrecht University, The Netherlands Florian Barth and Stefan Funke, Universität 2:25-2:45 Few Matches or Almost Stuttgart, Germany; Sabine Storandt, 2:25-2:45 Contraction Decomposition Periodicity: Faster Pattern Matching Universität Konstanz, Germany in Unit Disk Graphs and Algorithmic with Mismatches in Compressed Texts Applications in Parameterized Karl Bringmann, Marvin Künnemann, and 2:50-3:10 Concatenated k-Path Complexity Philip Wellnitz, Max Planck Institute for Covers Meirav Zehavi, Ben-Gurion University, Informatics, Germany Moritz Beck, Universität Konstanz, Germany; Israel; Fahad Panolan, University of Kam-Yiu Lam, City University of Hong 2:50-3:10 Lower Bounds for Text Bergen, Norway; Saket Saurabh, Institute Kong, Hong Kong; Joseph Kee Yin Ng, Indexing with Mismatches and of Mathematical Sciences, India and Hong Kong Baptist University, Hong Differences University of Bergen, Norway Kong; Sabine Storandt, Universität Tatiana Starikovskaya, École Normale Konstanz, Germany; Chun Jiang Zhu, 2:50-3:10 Polynomial-time Supérieure Paris, France; Vincent Cohen- University of Connecticut, U.S. Approximation Scheme for Minimum Addad, Sorbonne Universités and CNRS, k-Cut in Planar and Minor-free France; Laurent Feuilloley, Universite 3:15-3:35 Batch-Parallel Euler Tour Graphs Paris Diderot, France Trees Alireza Farhadi, University of Maryland, Thomas Tseng, Laxman Dhulipala, and Guy 3:15-3:35 Efficiently Approximating U.S.; MohammadHossein Bateni, Google Blelloch, Carnegie Mellon University, U.S. Edit Distance Between Research, U.S.; MohammadTaghi Pseudorandom Strings Hajiaghayi, University of Maryland, William Kuszmaul, Massachusetts Institute College Park, U.S. of Technology, U.S. 3:15-3:35 Embedding Planar 3:40-4:00 Approximating Lcs in Linear Graphs into Low-treewidth Graphs Time: Beating the √n Barrier with Applications to Efficient Saeed Seddighin and MohammadTaghi Approximation Schemes for Metric Hajiaghayi, University of Maryland, Problems Eli Fox-Epstein, Tufts University, U.S.; College Park, U.S.; Masoud Seddighin, Philip Klein, Brown University, U.S.; Sharif University of Technology, Iran; Aaron Schild, University of California, Xiaorui Sun, University of Illinois at Berkeley, U.S. Chicago, U.S. 3:40-4:00 A PTAS for Euclidean TSP with Hyperplane Neighborhoods Antonios Antoniadis and Krzysztof Fleszar, Max Planck Institute for Informatics, Germany; Ruben Hoeksma, Universität Bremen, Germany; Kevin Schewior, Technische Universität München, Germany and École Normale Supérieure, France Conference Program SODA, ALENEX, ANALCO and SOSA 2019 21
Monday, January 7 Monday, January 7 Monday, January 7 CP20 CP21 CP22 SODA Session 5C ALENEX Session 3 SODA Session 6A 2:00 PM-4:05 PM 4:30 PM-6:10 PM 4:30 PM-6:35 PM Room: Diamond 1 - 2nd Floor Room: Diamond 2 - 2nd Floor Room: Emerald Ballroom - 2nd Floor Chair: To Be Determined Chair: Cliff Stein, Columbia University, U.S. Chair: To Be Determined 2:00-2:20 The Maximum Number of 4:30-4:50 A New Integer Linear 4:30-4:50 Strategies for Stable Merge Minimal Dominating Sets in a Tree Program for the Steiner Tree Problem Sorting Günter Rote, Freie Universität Berlin, with Revenues, Budget and Hop Samuel Buss and Alexander Knop, University Germany Constraints of California, San Diego, U.S. Adalat Jabrayilov and Petra Mutzel, 2:25-2:45 How to Guess an -Digit 4:55-5:15 A Sort of an Adversary n Technische Universität Dortmund, Number Or Zamir, Haim Kaplan, and Uri Zwick, Tel Germany Zilin Jiang, Massachusetts Institute of Aviv University, Israel 4:55-5:15 SAT-Encodings for Treecut Technology, U.S.; Nikita Polyanskii, 5:20-5:40 A New Path from Splay to Width and Treedepth Skolkovo Institute of Science and Dynamic Optimality Technology, Russia Robert Ganian and Neha Lodha, Technische Caleb Levy, Princeton University, U.S.; Universität Wien, Austria; Sebastian 2:50-3:10 Vector Clique Robert Tarjan, Princeton University and Ordyniak, University of Sheffield, United Decompositions Intertrust Technologies, U.S. Kingdom; Stefan Szeider, Technische Raphael Yuster, University of Haifa, Israel Universität Wien, Austria 5:45-6:05 A Faster External Memory 3:15-3:35 Four-Coloring P -Free Priority Queue with DecreaseKeys 6 5:20-5:40 Efficiently Enumerating Graphs Shunhua Jiang, Tsinghua University, China; Hitting Sets of Hypergraphs Arising in Maria Chudnovsky and Sophie Spirkl, Data Profiling Kasper G. Larsen, Aarhus University, Princeton University, U.S.; Mingxian Thomas Bläsius, Tobias Friedrich, and Denmark Zhong, Lehman College, CUNY, U.S. Julius Lischeid, Universität Potsdam, 6:10-6:30 Optimal Construction of 3:40-4:00 Polynomial-time Algorithm Germany; Kitty Meeks, University of Compressed Indexes for Highly for Maximum Weight Independent Set Glasgow, Scotland, United Kingdom; Repetitive Texts on P6-Free Graphs Martin Schirneck, Universität Potsdam, Dominik Kempa, University of Warwick, Michal Pilipczuk, University of Warsaw, Germany United Kingdom Poland; Andrzej Grzesik, Jagiellonian 5:45-6:05 Exactly Solving the University, Poland; Tereza Klimošová, Maximum Weight Independent Set Charles University, Czech Republic; Marcin Problem on Large Real-world Graphs Pilipczuk, University of Warsaw, Poland Darren Strash, Colgate University, U.S.; Sebastian Lamm, Karlsruhe Institute of Technology, Germany; Christian Schulz, University of Vienna, Austria Coffee Break and Karlsruhe Institute of Technology, 4:05 PM-4:30 PM Germany; Robert Williger, Karlsruhe Institute of Technology, Germany; Room:Ballroom Foyer - 2nd Floor Huashuo Zhang, Colgate University, U.S. 22 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Monday, January 7 Monday, January 7 Tuesday, January 8 CP23 CP24 SODA Session 6B SODA Session 6C Registration 4:30 PM-6:35 PM 4:30 PM-6:35 PM 8:00 AM-5:00 PM Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Room: Registration Desk - 2rd Floor Chair: To Be Determined Chair: To Be Determined 4:30-4:50 Approximability of P -> Q 4:30-4:50 Towards Tight(er) Bounds for Matrix Norms: Generalized Krivine the Excluded Grid Theorem Rounding and Hypercontractive Julia Chuzhoy, Toyota Technological Continental Breakfast Hardness Institute at Chicago, U.S.; Zihan Tan, 8:30 AM-9:00 AM Vijay Bhattiprolu, Carnegie Mellon University of Chicago, U.S. Room: Ballroom Foyer - 2nd Floor University, U.S.; Mrinalkanti Ghosh, 4:55-5:15 Polynomial Planar Directed Toyota Technological Institute at Chicago, Grid Theorem U.S.; Venkatesan Guruswami, Carnegie Meike Hatzel, Technische Universität Berlin, Mellon University, U.S.; Euiwoong Lee, Germany; Ken-ichi Kawarabayashi, New York University, U.S.; Madhur National Institute of Informatics, Japan; Tulsiani, Toyota Technological Institute at Stephan Kreutzer, Technische Universität Chicago, U.S. Berlin, Germany 4:55-5:15 Proportional Volume 5:20-5:40 A Tight Erdös-Pósa Function Sampling and Approximation for Planar Minors Algorithms for A-Optimal Design Jean-Florent Raymond, Technische Uthaipon Tantipongpipat and Mohit Singh, Universität Berlin, Germany; Wouter Georgia Institute of Technology, U.S.; Cames Van Batenburg, Tony Huynh, Aleksandar Nikolov, University of and Gwenaël Joret, Université Libre de Toronto, Canada Bruxelles, Belgium 5:20-5:40 Perron-Frobenius Theory 5:45-6:05 Polynomial Bounds for in Nearly Linear Time: Positive Centered Colorings on Proper Minor- Eigenvectors, M-Matrices, Graph Closed Graph Classes Kernels, and Other Applications Sebastian Siebertz, Humboldt University Amirmahdi Ahmadinejad, Arun Jambulapati, at Berlin, Germany; Michal Pilipczuk, Amin Saberi, and Aaron Sidford, Stanford University of Warsaw, Poland University, U.S. 6:10-6:30 Every Collinear Set in a 5:45-6:05 Iterative Refinement for Planar Graph is Free ℓp-Norm Regression Vida Dujmovic, University of Ottawa, Sushant Sachdeva and Deeksha Adil, Canada; Fabrizio Frati, Universita Roma University of Toronto, Canada; Rasmus Tre, Italy; Daniel Gonçalves, Université de Kyng, Yale University, U.S.; Richard Montpellier and CNRS, France; Pat Morin, Peng, Georgia Institute of Technology, Carleton University, Canada; Günter Rote, U.S. Freie Universität Berlin, Germany 6:10-6:30 Optimizing Quantum Optimization Algorithms via Faster Quantum Gradient Computation Andras Gilyen, QuSoft, CWI and University Intermission of Amsterdam, Netherlands; Srinivasan 6:35 PM-6:45 PM Arunachalam, Massachusetts Institute of Technology, U.S.; Nathan Wiebe, Microsoft Research, U.S. SODA Business Meeting and Awards Presentation 6:45 PM-7:45 PM Room: Emerald Ballroom - 2nd Floor Conference Program SODA, ALENEX, ANALCO and SOSA 2019 23
Tuesday, January 8 Tuesday, January 8 Tuesday, January 8 CP25 CP26 CP27 ALENEX Session 4 SODA Session 7A SODA Session 7B 9:00 AM-11:05 AM 9:00 AM-11:05 AM 9:00 AM-11:05 AM Room: Diamond 2 - 2nd Floor Room: Emerald Ballroom - 2nd Floor Room: Topaz - 2nd Floor Chair: Stephen Kobourov, University of Chair: To Be Determined Chair: To Be Determined Arizona, U.S. 9:00-9:20 A 1.5-Approximation for Path 9:00-9:20 Coresets Meet EDCS: 9:00-9:20 Parallel Range, Segment TSP Algorithms for Matching and Vertex and Rectangle Queries with Rico Zenklusen, ETH Zürich, Switzerland Cover on Massive Graphs Augmented Maps 9:25-9:45 A New Dynamic Sepehr Assadi, University of Pennsylvania, Yihan Sun and Guy Blelloch, Carnegie Programming Approach for Spanning U.S.; MohammadHossein Bateni, Google Mellon University, U.S. Trees with Chain Constraints and Research, U.S.; Aaron Bernstein, Rutgers University, U.S.; Vahab Mirrokni, Google, 9:25-9:45 A Practical Algorithm for Beyond Martin Nagele and Rico Zenklusen, ETH Inc., U.S.; Cliff Stein, Columbia University, Spatial Agglomerative Clustering U.S. Thom Castermans, Bettina Speckmann, and Zürich, Switzerland Kevin Verbeek, Eindhoven University of 9:50-10:10 Lift and Project Algorithms 9:25-9:45 Sparsifying Distributed Technology, Netherlands for Precedence Constrained Algorithms with Ramifications in Scheduling to Minimize Completion Massively Parallel Computation and 9:50-10:10 Practical Methods for Centralized Local Computation Computing Large Covering Tours and Time Janardhan Kulkarni, Microsoft Research, Mohsen Ghaffari and Jara Uitto, ETH Zürich, Cycle Covers with Turn Cost Switzerland Sándor Fekete and Dominik M. Krupke, U.S.; Shashwat Garg, Eindhoven University Technische Universität Braunschweig, of Technology, Netherlands; Shi Li, State 9:50-10:10 Massively Parallel Germany University of New York at Buffalo, U.S. Approximation Algorithms for Edit Distance and Longest Common 10:15-10:35 A Polynomial Time 10:15-10:35 Faster Support Vector Subsequence Machines Constant Approximation For Minimizing Total Weighted Flow-time Saeed Seddighin, University of Maryland, Sebastian Schlag and Matthias Schmitt, College Park, U.S.; Xiaorui Sun, Janardhan Kulkarni, Microsoft Research, Karlsruhe Institute of Technology, University of Illinois at Chicago, U.S.; U.S.; Uriel Feige, Weizmann Institute of Germany; Christian Schulz, University of MohammadTaghi Hajiaghayi, University of Science, Israel; Shi Li, State University of Vienna, Austria and Karlsruhe Institute of Maryland, College Park, U.S. Technology, Germany New York at Buffalo, U.S. 10:15-10:35 Low Congestion Cycle 10:40-11:00 On Approximating 10:40-11:00 Scalable Edge Partitioning Covers and Their Applications Darren Strash, Colgate University, U.S.; (Sparse) Covering Integer Programs Eylon Yogev and Merav Parter, Weizmann Sebastian Schlag, Karlsruhe Institute Chandra Chekuri and Kent Quanrud, Institute of Science, Israel of Technology, Germany; Christian University of Illinois at Urbana-Champaign, Schulz, University of Vienna, Austria U.S. 10:40-11:00 Distributed Algorithms and Karlsruhe Institute of Technology, Made Secure: A Graph Theoretic Germany; Daniel Seemaier, Karlsruhe Approach Institute of Technology, Germany Eylon Yogev and Merav Parter, Weizmann Institute of Science, Israel 24 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Tuesday, January 8 Tuesday, January 8 Tuesday, January 8 CP28 IP3 CP29 SODA Session 7C Inherent Trade-Offs in SOSA Session 1 9:00 AM-11:05 AM Algorithmic Fairness 2:00 PM-4:05 PM Room: Diamond 1 - 2nd Floor 11:30 AM-12:30 PM Room: Diamond 2 - 2nd Floor Chair: To Be Determined Room: Emerald Ballroom - 2nd Floor Chair: To Be Determined 9:00-9:20 Interval Vertex Deletion Chair: To Be Determined 2:00-2:20 Isotonic Regression by Admits a Polynomial Kernel Recent discussion in the public sphere Dynamic Programming Akanksha Agrawal, Hungarian Academy of about classification by algorithms has Günter Rote, Freie Universität Berlin, Germany Sciences, Hungary involved tension between competing 9:25-9:45 Losing Treewidth by notions of what it means for such a 2:25-2:45 An Illuminating Algorithm for Separating Subsets classification to be fair to different the Light Bulb Problem Euiwoong Lee, New York University, U.S.; groups. We consider several of the key Josh Alman, Massachusetts Institute of Anupam Gupta and Jason M. Li, Carnegie fairness conditions that lie at the heart Technology, U.S. Mellon University, U.S.; Pasin Manurangsi, of these debates, and discuss recent 2:50-3:10 Simple Concurrent University of California, Berkeley, U.S.; research establishing inherent trade- Labeling Algorithms for Connected Michal Wlodarczyk, University of Warsaw, Components Poland offs between these conditions. We also consider a variety of methods for Sixue Liu, Princeton University, U.S.; Robert 9:50-10:10 On r-Simple k-Path and promoting fairness and related notions Tarjan, Princeton University and Intertrust Related Problems Parameterized by Technologies, U.S. for classification and selection problems k/r 3:15-3:35 A Framework for Searching Meirav Zehavi, Ben-Gurion University, Israel; that involve sets rather than just in Graphs in the Presence of Errors Gregory Gutin and Magnus Wahlstrom, individuals. This talk is based on joint Dariusz Dereniowski, Gdansk University Royal Holloway, University of London, work with Sendhil Mullainathan, Manish of Technology, Poland; Stefan Tiegel, United Kingdom Raghavan, and Maithra Raghu. Przemyslaw Uznanski, and Daniel Wolleb- 10:15-10:35 A Time and Space- Jon M. Kleinberg Graf, ETH Zürich, Switzerland Optimal Algorithm for the Many-Visits Cornell University, U.S. TSP 3:40-4:00 Selection from Heaps, Row- André Berger, Maastricht University, Sorted Matrices and X+Y Using Soft Netherlands; László Kozma, Eindhoven Heaps Uri Zwick, Haim Kaplan, and Or Zamir, Tel University of Technology, Netherlands; Lunch Break Aviv University, Israel Matthias Mnich, Maastricht University, 12:30 PM-2:00 PM Netherlands and Universität Bonn, Germany; Roland Vincze, Maastricht Attendees on their own University, Netherlands 10:40-11:00 Quantum Speedups for Exponential-Time Dynamic Programming Algorithms Jevgenijs Vihrovs, Andris Ambainis, Kaspars Balodis, Janis Iraids, Martins Kokainis, and Krisjanis Prusis, University of Latvia, Latvia
Coffee Break 11:05 AM-11:30 AM Room: Ballroom Foyer - 2nd Floor Conference Program SODA, ALENEX, ANALCO and SOSA 2019 25
Tuesday, January 8 Tuesday, January 8 Tuesday, January 8 CP30 CP31 CP32 SODA Session 8A SODA Session 8B SODA Session 8C 2:00 PM-4:05 PM 2:00 PM-4:05 PM 2:00 PM-4:05 PM Room: Emerald Ballroom - 2nd Floor Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: To Be Determined 2:00-2:20 Optimal Las Vegas 3:40-4:00 Deterministically Maintaining 2:00-2:20 Prophet Secretary Through Approximate Near Neighbors in ℓp a (2 +ϵε)-Approximate Minimum Blind Strategies Alexander Wei, Harvard University, U.S. Vertex Cover in O(1/ε2) Amortized José Correa and Raimundo Saona, 2:25-2:45 The Andoni-Krauthgamer- Update Time Universidad de Chile, Chile; Bruno Ziliotto, Razenshteyn Characterization of Sayan Bhattacharya, University of Warwick, Universite Paris Dauphine and CNRS, Sketchable Norms Fails for Sketchable United Kingdom; Janardhan Kulkarni, France Microsoft Research, U.S. Metrics 2:25-2:45 Pricing for Online Resource Assaf Naor, Princeton University, U.S.; 2:00-2:20 (1 + Eps)-Approximate Allocation: Intervals and Paths Subhash Khot, New York University, U.S. Incremental Matching in Constant Shuchi Chawla, Benjamin Miller, and Yifeng Deterministic Amortized Time Teng, University of Wisconsin, Madison, 2:50-3:10 Tight Bounds for ℓp Oblivious Subspace Embeddings Chris Schwiegelshohn, Università di Roma U.S. Ruosong Wang and David Woodruff, Carnegie “La Sapienza”, Italy; Fabrizio Grandoni, 2:50-3:10 An Optimal Truthful Mellon University, U.S. IDSIA, Switzerland; Stefano Leonardi, Mechanism for the Online Weighted Università di Roma “La Sapienza”, Bipartite Matching Problem 3:15-3:35 Optimal Lower Bounds for Italy; Piotr Sankowski, University of Distributed and Streaming Spanning Rebecca Reiffenhaeuser, Università di Roma Warsaw, Poland; Shay Solomon, Tel Aviv “La Sapienza”, Italy Forest Computation University, Israel Huacheng Yu and Jelani Nelson, Harvard 3:15-3:35 Fully Polynomial-Time University, U.S. 2:25-2:45 A Deamortization Approach Approximation Schemes for Fair Rent for Dynamic Spanner and Dynamic Division 3:40-4:00 Communication-Rounds Maximal Matching Eshwar Ram Arunachaleswaran, Siddharth Tradeoffs for Common Randomness Aaron Bernstein, Rutgers University, U.S.; Barman, and Nidhi Rathi, Indian Institute and Secret Key Generation Sebastian Forster, University of Salzburg, of Science, India Mitali Bafna, Harvard University, U.S.; Germany; Henzinger Monika, University Badih Ghazi, Google Research, U.S.; Noah of Vienna, Austria 3:40-4:00 Communication Complexity Golowich and Madhu Sudan, Harvard of Discrete Fair Division University, U.S. 2:50-3:10 Fully Dynamic Maximal Benjamin Plaut and Tim Roughgarden, Independent Set with Sublinear in n Stanford University, U.S. Update Time Sepehr Assadi, University of Pennsylvania, U.S.; Krzysztof Onak, IBM Research, U.S.; Baruch Schieber, New Jersey Coffee Break Institute of Technology, U.S.; Shay Solomon, Tel Aviv University, Israel 4:05 PM-4:30 PM 3:15-3:35 Dynamic Edge Coloring with Room: Ballroom Foyer - 2nd Floor Improved Approximation Ran Duan, Haoqing He, and Tianyi Zhang, Tsinghua University, China 26 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Tuesday, January 8 Tuesday, January 8 Tuesday, January 8 CP33 CP34 CP35 SOSA Session 2 SODA Session 9A SODA Session 9B 4:30 PM-6:35 PM 4:30 PM-6:35 PM 4:30 PM-6:35 PM Room: Diamond 2 - 2nd Floor Room: Emerald Ballroom - 2nd Floor Room: Topaz - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: To Be Determined 4:30-4:50 Approximating Optimal 4:30-4:50 The I/O Complexity of 4:30-4:50 Analyzing Boolean Functions Transport with Linear Programs Toom-Cook Integer Multiplication on the Biased Hypercube Via Higher- Kent Quanrud, University of Illinois at Lorenzo De Stefani, Brown University, U.S.; Dimensional Agreement Tests Urbana-Champaign, U.S. Gianfranco Bilardi, University of Padova, Yuval Filmus, Technion Israel Institute of Technology, Israel; Irit Dinur, Weizmann 4:55-5:15 LP Relaxation and Tree Italy Institute of Science, Israel; Prahladh Packing for Minimum k-Cuts 4:55-5:15 I/O-Efficient Algorithms Harsha, Tata Institute of Fundamental Chandra Chekuri and Kent Quanrud, for Topological Sort and Related Research, India University of Illinois at Urbana-Champaign, Problems U.S.; Chao Xu, Yahoo! Research, U.S. Nairen Cao, Jeremy Fineman, Katina 4:55-5:15 List Decoding with Double Samplers 5:20-5:40 On Primal-Dual Circle Russell, and Eugene Yang, Georgetown Inbal R. Livni Navon and Irit Dinur, Representations University, U.S. Weizmann Institute of Science, Israel; Stefan Felsner, Technische Universität, Berlin, 5:20-5:40 On the Structure of Unique Prahladh Harsha, Tata Institute of Germany; Günter Rote, Freie Universität Shortest Paths in Graphs Fundamental Research, India; Tali Berlin, Germany Greg Bodwin, Georgia Institute of Kaufman, Massachusetts Institute of Technology, U.S. 5:45-6:05 Asymmetric Convex Technology, U.S.; Amnon Ta-Shma, Tel Intersection Testing 5:45-6:05 Near Optimal Algorithms For Aviv University, Israel Luis Barba, ETH Zürich, Switzerland; The Single Source Replacement Paths 5:20-5:40 Maximally Recoverable Wolfgang J. Mulzer, Freie Universität Problem LRCs: A Field Size Lower Bound and Berlin, Germany Sarel Cohen and Shiri Chechik, Tel Aviv Constructions for Few Heavy Parities University, Israel 6:10-6:30 Relaxed Voronoi: A Simple Sivakanth Gopi, Microsoft Research, U.S.; Framework for Terminal-Clustering 6:10-6:30 Exact Distance Oracles for Venkatesan Guruswami, Carnegie Mellon Problems Planar Graphs with Failing Vertices University, U.S.; Sergey Yekhanin, Arnold Filtser, Ben Gurion University, Israel; Panagiotis Charalampopoulos, King’s Microsoft, U.S. Robert Krauthgamer and Ohad Trabelsi, College London, United Kingdom; 5:45-6:05 Binary Robust Positioning Weizmann Institute of Science, Israel Shay Mozes and Benjamin Tebeka, Patterns with Low Redundancy and Interdisciplinary Center Herzliya, Israel Efficient Locating Algorithms Yeow Meng Chee, Duc Tu Dao, Han Mao Kiah, San Ling, and Hengjia Wei, Nanyang Technological University, Singapore 6:10-6:30 Synchronization Strings: Highly Efficient Deterministic Constructions over Small Alphabets Kuan Cheng, Johns Hopkins University, U.S.; Bernhard Haeupler, Carnegie Mellon University, U.S.; Xin Li, Johns Hopkins University, U.S.; Amirbehshad Shahrasbi, Carnegie Mellon University, U.S.; Ke Wu, Johns Hopkins University, U.S. Conference Program SODA, ALENEX, ANALCO and SOSA 2019 27
Tuesday, January 8 Wednesday, Wednesday, January 9 CP36 January 9 CP37 SODA Session 9C SOSA Session 3 4:30 PM-6:35 PM 9:00 AM-11:05 AM Registration Room: Diamond 1 - 2nd Floor Room: Diamond 2 - 2nd Floor 8:00 AM-5:00 PM Chair: To Be Determined Chair: To Be Determined 4:30-4:50 The Complexity of Room: Registration Desk - 2rd Floor 9:00-9:20 Towards a Unified Theory of Approximately Counting Retractions Sparsification for Matching Problems Jacob Focke, Leslie Ann Goldberg, and Sepehr Assadi, University of Pennsylvania, Stanislav Zivny, University of Oxford, Continental Breakfast U.S.; Aaron Bernstein, Rutgers University, United Kingdom U.S. 8:30 AM-9:00 AM 4:55-5:15 Improved Bounds for 9:25-9:45 A New Application of Randomly Sampling Colorings Via Room: Ballroom Foyer - 2nd Floor Orthogonal Range Searching for Linear Programming Computing Giant Graph Diameters Sitan Chen, Massachusetts Institute of Guillaume Ducoffe, ICI – National Institute Technology, U.S.; Michelle Delcourt, for Research and Development informatics, University of Waterloo, Canada; Ankur Romania Moitra, Massachusetts Institute of Technology, U.S.; Guillem Perarnau, 9:50-10:10 Simplified and Space-Optimal Semi-Streaming University of Birmingham, United (2+ )-Approximate Matching Kingdom; Luke Postle, University of ε Mohsen Ghaffari, ETH Zürich, Switzerland; Waterloo, Canada David Wajc, Carnegie Mellon University, 5:20-5:40 Algorithms for #{BIS}-Hard U.S. Problems on Expander Graphs 10:15-10:35 Simple Greedy Matthew Jenssen and Peter Keevash, 2-Approximation Algorithm for the University of Oxford, United Kingdom; Maximum Genus of a Graph Will Perkins, University of Illinois at Michal Kotrbcik and Martin Skoviera, Chicago, U.S. Comenius University, Bratislava, Slovakia 5:45-6:05 Approximability of the Six- 10:40-11:00 A Note on Max K-Vertex Vertex Model Cover: Faster Fpt-As, Smaller Jin-Yi Cai and Tianyu Liu, University of Approximate Kernel and Improved Wisconsin, Madison, U.S.; Pinyan Lu, Approximation Shanghai University of Finance and Pasin Manurangsi, University of California, Economics, China Berkeley, U.S. 6:10-6:30 Zeros of Holant Problems: Locations and Algorithms Heng Guo, University of Edinburgh, United Kingdom; Chao Liao, Shanghai Jiao Tong University, China; Pinyan Lu, Shanghai University of Finance and Economics, China; Chihao Zhang, Shanghai Jiaotong University, China
Intermission 6:35 PM-6:45 PM
SOSA Business Meeting 6:45 PM-7:45 PM Room: Diamond 2 - 2nd Floor 28 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Wednesday, January 9 Wednesday, January 9 Wednesday, January 9 CP38 CP39 CP40 SODA Session 10A SODA Session 10B SODA Session 10C 9:00 AM-11:05 AM 9:00 AM-11:05 AM 9:00 AM-11:05 AM Room: Emerald Ballroom - 2nd Floor Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: To Be Determined 9:00-9:20 On Facility Location with 9:00-9:20 Theorems of Carathéodory, 9:00-9:20 Can We Overcome the n log n General Lower Bounds Helly, and Tverberg Without Barrier for Oblivious Sorting? Shi Li, State University of New York at Dimension Wei-Kai Lin and Elaine Shi, Cornell Buffalo, U.S. Karim Adiprasito, Hebrew University of University, U.S.; Tiancheng Xie, Shanghai Jerusalem, Israel; Imre Bárány, Alfréd Jiao Tong University, China 9:25-9:45 Hierarchical Clustering Rényi Institute of Mathematics, Budapest Better Than Average-Linkage 9:25-9:45 Lower Bounds for Oblivious and University College London, United Moses Charikar, Vaggos Chatziafratis, and Data Structures Kingdom; Nabil H. Mustafa, ESIEE, Rad Niazadeh, Stanford University, U.S. Riko Jacob, IT University of Copenhagen, France Denmark; Kasper G. Larsen and Jesper B. 9:50-10:10 The Threshold for SDP- 9:25-9:45 On the Spanning and Nielsen, Aarhus University, Denmark Refutation of Random Regular NAE- Routing Ratio of Theta-Four 3SAT 9:50-10:10 Foundations of Differentially Darryl R. Hill and Prosenjit Bose, Carleton Tselil Schramm, Massachusetts Institute of Oblivious Algorithms University, Canada; Jean-Lou De Carufel, Technology and Harvard University, U.S.; T-H. Hubert Chan, University of Hong Kong, University of Ottawa, Canada; Michiel Yash Deshpande, Massachusetts Institute Hong Kong; Kai-Min Chung, Academia Smid, Carleton University, Canada of Technology, U.S.; Andrea Montanari, Sinica, Taiwan; Bruce M. Maggs, Duke Stanford University, U.S.; Ryan O’Donnell, 9:50-10:10 Greedy Spanners Are University and Akamai Technologies, U.S.; Carnegie Mellon University, U.S.; Optimal in Doubling Metrics Elaine Shi, Cornell University, U.S. Subhabrata Sen, Massachusetts Institute of Glencora Borradaile and Hung Le, Oregon 10:15-10:35 Amplification by Shuffling: Technology, U.S. State University, U.S.; Christian Wulff- From Local to Central Differential Nilsen, University of Copenhagen, 10:15-10:35 Exponential Lower Bounds Privacy via Anonymity Denmark on Spectrahedral Representations of Ulfar Erlingsson, Vitaly Feldman, Ilya Hyperbolicity Cones 10:15-10:35 Viewing the Rings of a Mironov, Ananth Raghunathan, and Kunal Nick Ryder, Nikhil Srivastava, and Prasad Tree: Minimum Distortion Embeddings Talwar, Google, Inc., U.S.; Abhradeep Raghavendra, University of California, into Trees Thakurta, Google and University of Berkeley, U.S.; Benjamin Weitz, Pintrest, Benjamin A. Raichel, University of Texas at California, Santa Cruz, U.S. U.S. Dallas, U.S.; Amir Nayyeri, Oregon State 10:40-11:00 Towards Instance-Optimal University, U.S. 10:40-11:00 Universal Trees Grow Private Query Release Aleksandar Nikolov, University of Toronto, Inside Separating Automata: Quasi- 10:40-11:00 Minimizing Interference Polynomial Lower Bounds for Parity Potential Among Moving Entities Canada; Jaroslaw Blasiok, Harvard Games Daniel Busto, Ericsson, Montreal, Canada; University, U.S.; Mark Bun, Princeton Wojciech Czerwinski, University of Warsaw, William S. Evans, University of British University, U.S.; Thomas Steinke, IBM Poland; Laure Daviaud, University of Columbia, Canada; David Kirkpatrick, Research, U.S. Warwick, United Kingdom; Nathanael University of British Columbia, Canada Fijalkow, Université Bordeaux, France; Marcin Jurdzinski and Ranko Lazic, University of Warwick, United Kingdom; Coffee Break Pawel Parys, University of Warsaw, Poland 11:05 AM-11:30 AM Room: Ballroom Foyer - 2nd Floor Conference Program SODA, ALENEX, ANALCO and SOSA 2019 29
Wednesday, January 9 Wednesday, January 9 Wednesday, January 9 IP4 CP41 CP42 (Hardness of) Approximation SOSA Session 4 SODA Session 11A and Expansion 2:00 PM-4:05 PM 2:00 PM-4:05 PM 11:30 AM-12:30 PM Room: Diamond 2 - 2nd Floor Room: Emerald Ballroom - 2nd Floor Room: Emerald Ballroom - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: To Be Determined 2:00-2:20 Simple Contention 2:00-2:20 Non-Empty Bins with Simple Recently, there has been some exciting Resolution Via Multiplicative Weight Tabulation Hashing progress towards proving the unique Updates Anders Aamand and Mikkel Thorup, Yi-Jun Chang, Wenyu Jin, and Seth Pettie, games conjecture. Is the conjecture University of Copenhagen, Denmark University of Michigan, U.S. true? Experts are not sure. Still, 2:25-2:45 Derandomized Balanced it is clearly key to understanding 2:25-2:45 A Simple Near-Linear Allocation approximation algorithms for many Pseudopolynomial Time Randomized Xue Chen, Northwestern University, U.S. Algorithm for Subset Sum optimization problems, most notably 2:50-3:10 Optimal Ball Recycling Ce Jin and Hongxun Wu, Tsinghua constraint satisfaction. I will describe Michael A. Bender and Jake Christensen, University, China the background and consequences of Stony Brook University, U.S.; Alexander the conjecture and then focus on some 2:50-3:10 Submodular Optimization in Conway and Martin Farach-Colton, Rutgers interesting technical step pertaining to the MapReduce Model University, U.S.; Rob Johnson, Vmware the expansion of the so-called Grassman Paul Liu and Jan Vondrak, Stanford Research, U.S.; Meng-Tsung Tsai, National University, U.S. graph. I will then describe a more general Chiao Tung University, Taiwan phenomenon called high dimensional 3:15-3:35 Compressed Sensing with 3:15-3:35 A Fourier-Analytic Approach expansion (HDX). HDX is family of a Adversarial Sparse Noise Via L1 for the Discrepancy of Random Set generalizations of expansion in graphs to Regression Systems hypergraphs. This is a new area of study Sushrut Karmalkar and Eric Price, University Rebecca Hoberg and Thomas Rothvoss, of Texas at Austin, U.S. and has potential for algorithms, as I will University of Washington, U.S. try to show. 3:40-4:00 Approximating Maximin 3:40-4:00 On the Discrepancy of Share Allocations Random Low Degree Set Systems Jugal Garg, Peter McGlaughlin, and Setareh Nikhil Bansal, Centrum voor Wiskunde Irit Dinur Taki, University of Illinois at Urbana- en Informatica (CWI) and Eindhoven Weizmann Institute of Science, Israel Champaign, U.S. University of Technology, Netherlands; Raghu Meka, University of California, Los Angeles, U.S. Lunch Break 12:30 PM-2:00 PM Attendees on their own 30 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Wednesday, January 9 Wednesday, January 9 Wednesday, January 9 CP43 CP44 CP45 SODA Session 11B SODA Session 11C SODA Session 12A 2:00 PM-4:05 PM 2:00 PM-4:05 PM 4:30 PM-6:35 PM Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Room: Emerald Ballroom - 2nd Floor Chair: To Be Determined Chair: To Be Determined Chair: To Be Determined 2:00-2:20 Optimal Lower Bounds for 2:00-2:20 Constructive Polynomial 4:30-4:50 Dimension-independent Sketching Graph Cuts Partitioning for Algebraic Curves In~ Sparse Fourier Transform Charles A. Carlson and Alexandra Kolla, \reals3 with Applications Michael Kapralov, IBM T.J. Watson University of Colorado Boulder, U.S.; Esther Ezra, Georgia Institute of Technology, Research Center, U.S.; Ameya Velingker, Nikhil Srivastava and Luca Trevisan, U.S.; Boris Aronov, New York University, Google, Inc., U.S.; Amir Zandieh, École University of California, Berkeley, U.S. U.S.; Joshua Zahl, University of British Polytechnique Fédérale de Lausanne, Columbia, Canada Switzerland 2:25-2:45 Spectral Sparsification of Hypergraphs 2:25-2:45 Computing Height 4:55-5:15 Adaptive Sparse Recovery Tasuku Soma, University of Tokyo, Japan; Persistence and Homology Generators with Limited Adaptivity 3 Yuichi Yoshida, National Institute of in R Efficiently Akshay Kamath and Eric Price, University of Informatics, Japan Tamal K. Dey, Ohio State University, U.S. Texas at Austin, U.S. 2:50-3:10 Cheeger Inequalities for 2:50-3:10 Hardness of Approximation 5:20-5:40 Efficient Algorithms and Submodular Transformations for Morse Matching Lower Bounds for Robust Linear Yuichi Yoshida, National Institute of Abhishek J. Rathod and Ulrich Bauer, Regression Informatics, Japan Technische Universität München, Germany Weihao Kong, Stanford University, U.S.; Ilias Diakonikolas, University of Southern 3:15-3:35 Short Cycles via Low- 3:15-3:35 Improved Topological California, U.S.; Alistair Stewart, Stanford diameter Decompositions Approximations by Digitization University, U.S. Yang Liu, Stanford University, U.S.; Sushant Aruni Choudhary, Freie Universität Sachdeva and Zejun Yu, University of Berlin, Germany; Michael Kerber, Graz 5:45-6:05 High-Dimensional Robust Toronto, Canada University of Technology, Austria; Sharath Mean Estimation in Nearly-linear Time Raghvendra, Virginia Tech, U.S. Yu Cheng, Duke University, U.S.; Ilias 3:40-4:00 Expander Decomposition Diakonikolas, University of Southern and Pruning: Faster, Stronger, and 3:40-4:00 Full Tilt: Universal California, U.S.; Rong Ge, Duke Simpler. Constructors for General Shapes with University, U.S. Thatchaphol Saranurak, Toyota Technological Uniform External Forces Institute at Chicago, U.S.; Di Wang, Jose Balanza-Martinez, David Caballero, 6:10-6:30 Relative Error Tensor Low Georgia Institute of Technology, U.S. Angel Cantu, Luis Garcia, Austin Rank Approximation Luchsinger, and Rene Reyes, University Zhao Song, University of Texas at Austin, of Texas, Rio Grande Valley; Robert T. U.S.; David Woodruff, Carnegie Mellon Schweller, University of Texas - Pan University, U.S.; Peilin Zhong, Columbia American, U.S.; Tim Wylie, University of University, U.S. Texas, Rio Grande Valley
Coffee Break 4:05 PM-4:30 PM Room: Ballroom Foyer - 2nd Floor Conference Program SODA, ALENEX, ANALCO and SOSA 2019 31
Wednesday, January 9 Wednesday, January 9 CP46 CP47 SODA Session 12B SODA Session 12C 4:30 PM-6:35 PM 4:30 PM-6:35 PM Room: Topaz - 2nd Floor Room: Diamond 1 - 2nd Floor Chair: To Be Determined Chair: To Be Determined 4:30-4:50 Popular Matchings and Limits 4:30-4:50 Seth Says: Weak Fréchet to Tractability Distance is Faster, But Only if it is Yuri Faenza, Columbia University, U.S.; Continuous and in One Dimension Telikepalli Kavitha, Tata Institute of Tim Ophelders, Michigan State University, Fundamental Research, India; Vladlena U.S.; Kevin Buchin and Bettina Powers and Xingyu Zhang, Columbia Speckmann, Eindhoven University of University, U.S. Technology, Netherlands 4:30-4:50 Popular Matching in 4:55-5:15 Fréchet Distance under Roommates Setting is Np-Hard Translation: Conditional Hardness and Pranabendu Misra and Sushmita Gupta, an Algorithm via Offline Dynamic Grid University of Bergen, Norway; Saket Reachability Saurabh, Institute of Mathematical Sciences, Karl Bringmann, Marvin Künnemann, and India and University of Bergen, Norway; André Nusser, Max Planck Institute for Meirav Zehavi, Ben-Gurion University, Informatics, Germany Israel 5:20-5:40 Approximating (k,l)-Center 4:55-5:15 A (1+1/e)-Approximation Clustering for Curves Algorithm for Maximum Stable Kevin Buchin, Eindhoven University of Matching with One-sided Ties and Technology, Netherlands; Anne Driemel, Incomplete Lists Universität Bonn, Germany; Joachim Chi-Kit Lam and C. Gregory Plaxton, Gudmundsson, University of Sydney, University of Texas at Austin, U.S. Australia; Michael Horton, New York University, U.S. and University of Sydney, 5:20-5:40 Beating Greedy for Stochastic Australia; Irina Kostitsyna, Eindhoven Bipartite Matching University of Technology, Netherlands; Buddhima Gamlath, Sagar Kale, and Ola Maarten Loffler, Utrecht University, Svensson, École Polytechnique Fédérale de Netherlands; Martijn Struijs, Eindhoven Lausanne, Switzerland University of Technology, Netherlands 5:45-6:05 Stochastic Matching with Few 5:45-6:05 Analysis of Ward’s Method Queries: New Algorithms and Tools Anna Großwendt, Heiko Röglin, Melanie Soheil Behnezhad and Alireza Farhadi, Schmidt, and Clemens Rösner, Universität University of Maryland, U.S.; Bonn, Germany MohammadTaghi Hajiaghayi, University of Maryland, College Park, U.S.; Nima 6:10-6:30 Exact Algorithms and Reyhani, University of Maryland, U.S. Lower Bounds for Stable Instances of Euclidean K-Means 6:10-6:30 Tight Competitive Ratios of Zachary Friggstad, Kamyar Khodamoradi, Classic Matching Algorithms in the Fully and Mohammad Salavatipour, University of Online Model Alberta, Canada Zhiyi Huang, University of Hong Kong, Hong Kong; Binghui Peng, Tsinghua University, China; Zhihao Gavin Tang, University of Hong Kong, Hong Kong; Runzhou Tao, Tsinghua University, China; Xiaowei Wu, City University of Hong Kong, Hong Kong; Yuhao Zhang, University of Hong Kong, Hong Kong 32 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Speaker Index
Symposium on Simplicity in Algorithms (SOSA19) Conference Program SODA, ALENEX, ANALCO and SOSA 2019 33
A Chatziafratis, Vaggos, CP38, 9:25 Wed Genitrini, Antoine, CP1, 9:25 Sun Aamand, Anders, CP42, 2:00 Wed Chechik, Shiri, CP14, 9:25 Mon Ghaffari, Mohsen, CP14, 9:50 Mon Abdelkader, Ahmed, CP7, 2:00 Sun Chechik, Shiri, CP34, 5:45 Tue Ghaffari, Mohsen, CP27, 9:25 Tue Adil, Deeksha, CP23, 5:45 Mon Chen, Lijie, CP2, 9:25 Sun Gilyen, Andras, CP23, 6:10 Mon Agarwal, Arpit, CP6, 3:15 Sun Chen, Sitan, CP36, 4:55 Tue Golowich, Noah, CP30, 3:40 Tue Agrawal, Akanksha, CP28, 9:00 Tue Chen, Xue, CP42, 2:25 Wed Gopi, Sivakanth, CP35, 5:20 Tue Ahmadinejad, Amirmahdi, CP23, 5:20 Cheng, Yu, CP45, 5:45 Wed Grossman, Ofer, CP11, 4:30 Sun Mon Choudhary, Aruni, CP44, 3:15 Wed Gurjar, Rohit, CP15, 9:00 Mon Alman, Josh, CP29, 2:25 Tue Conway, Alexander, CP42, 2:50 Wed Antoniadis, Antonios, CP18, 3:40 Mon H Hackl, Benjamin, CP1, 10:15 Sun Argue, C.J., CP3, 9:50 Sun D de Panafieu, Elie, CP1, 9:00 Sun Harms, Nathaniel, CP12, 4:55 Sun Assadi, Sepehr, CP14, 9:00 Mon De Stefani, Lorenzo, CP34, 4:30 Tue Harris, David G., CP14, 10:40 Mon Assadi, Sepehr, CP27, 9:00 Tue Del Giudice, Nicola, CP9, 5:20 Sun Hass, Nikolaj, CP13, 9:25 Mon Assadi, Sepehr, CP37, 9:00 Wed Deng, Yuan, CP4, 9:25 Sun Hatzel, Meike, CP24, 4:55 Mon Axiotis, Kyriakos, CP2, 10:15 Sun Dereniowski, Dariusz, CP29, 3:15 Tue Heuberger, Clemens, CP1, 9:50 Sun Azais, Romain, CP13, 10:15 Mon Desmarais, Colin, CP9, 4:55 Sun Hill, Darryl R., CP39, 9:25 Wed B Dey, Tamal K., CP44, 2:25 Wed Hoberg, Rebecca, CP42, 3:15 Wed Balkanski, Eric, CP6, 2:25 Sun Dinur, Irit, IP4, 11:30 Wed Hovarau, Artsiom, CP8, 2:50 Sun Ban, Frank, CP12, 6:10 Sun Dinur, Irit, CP35, 4:30 Tue Huynh, Tony, CP24, 5:20 Mon Barba, Luis, CP33, 5:45 Tue Driemel, Anne, CP47, 5:20 Wed Barth, Florian, CP17, 2:25 Mon Ducoffe, Guillaume, CP37, 9:25 Wed J Jabrayilov, Adalat, CP21, 4:30 Mon Beck, Moritz, CP17, 2:50 Mon Jenssen, Matthew, CP36, 5:20 Tue Behnezhad, Soheil, CP46, 5:45 Wed E Ene, Alina, CP6, 2:25 Sun Jiang, Shunhua, CP22, 5:45 Mon Bei, Xiaohui, CP4, 9:50 Sun Eppstein, David, CP10, 6:10 Sun Jin, Ce, CP41, 2:25 Wed Bernstein, Aaron, CP31, 2:25 Tue Evans, William S., CP39, 10:40 Wed Jin, Yaonan, CP4, 10:15 Sun Bhattacharya, Sayan, CP31, 3:40 Tue Ezra, Esther, CP44, 2:00 Wed Jindal, Gorav, CP11, 5:45 Sun Bhattiprolu, Vijay, CP23, 4:30 Mon Bibak, Ali, CP15, 9:50 Mon F K Blasiok, Jaroslaw, CP40, 10:40 Wed Faenza, Yuri, CP46, 4:30 Wed Kamath, Akshay, CP45, 4:55 Wed Bodwin, Greg, CP34, 5:20 Tue Fahrbach, Matthew, CP6, 2:25 Sun Kamath, Gautam, CP12, 4:30 Sun Brakensiek, Joshua, CP8, 2:00 Sun Farhadi, Alireza, CP18, 2:50 Mon Karmalkar, Sushrut, CP41, 3:15 Wed Bringmann, Karl, CP2, 9:50 Sun Fichtenberger, Hendrik, CP12, 5:20 Sun Kaski, Petteri, CP8, 3:15 Sun Bubeck, Sebastien, CP3, 9:00 Sun Fill, James Allen, CP5, 3:15 Sun Kempa, Dominik, CP22, 6:10 Mon Bun, Mark, CP11, 6:10 Sun Filtser, Arnold, CP33, 6:10 Tue Khodamoradi, Kamyar, CP47, 6:10 Wed Focke, Jacob, CP36, 4:30 Tue Kleer, Pieter, CP16, 9:50 Mon C Frieze, Alan, CP16, 9:00 Mon Klein, Philip, CP18, 3:15 Mon Carlson, Charles A., CP43, 2:00 Wed Kleinberg, Jon M., IP3, 11:30 Tue Castermans, Thom, CP25, 9:25 Tue G Klimczak, Mateusz, CP1, 10:40 Sun Charalampopoulos, Panagiotis, CP34, Gamlath, Buddhima, CP46, 5:20 Wed Knop, Alexander, CP22, 4:30 Mon 6:10 Tue Garg, Mohit, CP6, 2:00 Sun Kociumaka, Tomasz, CP19, 2:00 Mon 34 SODA, ALENEX, ANALCO and SOSA 2019 Conference Program
Kong, Weihao, CP45, 5:20 Wed Mitzenmacher, Michael, CP9, 6:10 Sun Rote, Günter, CP24, 6:10 Mon Krupke, Dominik M., CP25, 9:50 Tue Molinaro, Marco, CP3, 9:25 Sun Rote, Günter, CP29, 2:00 Tue Kulkarni, Janardhan, CP26, 9:50 Tue Molinaro, Marco, CP6, 3:40 Sun Rote, Günter, CP33, 5:20 Tue Kulkarni, Janardhan, CP26, 10:15 Tue Mustafa, Nabil H., CP39, 9:00 Wed Russell, Katina, CP34, 4:55 Tue Ryder, Nick, CP38, 10:15 Wed Kurpicz, Florian, CP13, 9:50 Mon N Kuszmaul, William, CP19, 3:15 Mon Nagele, Martin, CP26, 9:25 Tue S L Naor, Assaf, CP30, 2:25 Tue Sabin, Manuel, CP16, 10:15 Mon Lahn, Nathaniel, CP10, 5:45 Sun Nebel, Markus, CP5, 2:50 Sun Sales, Marcelo, CP7, 3:40 Sun Lam, Chi-Kit, CP46, 4:55 Wed Nederlof, Jesper, CP18, 2:00 Mon Salmasi, Ario, CP10, 4:55 Sun Lamm, Sebastian, CP21, 5:45 Mon Nguyen, Huy L., CP8, 3:40 Sun Saona, Raimundo, CP32, 2:00 Tue Langowski, Simon H., CP5, 2:25 Sun Nusser, André, CP47, 4:55 Wed Schirneck, Martin, CP21, 5:20 Mon Larsen, Kasper G., CP40, 9:25 Wed Schlag, Sebastian, CP25, 10:15 Tue O Schramm, Tselil, CP38, 9:50 Wed Lazic, Ranko, CP38, 10:40 Wed Oh, Eunjin, CP7, 2:50 Sun Schwiegelshohn, Chris, CP31, 2:00 Tue Le, Hung, CP39, 9:50 Wed Oliveira, Roberto, CP9, 5:45 Sun Seddighin, Saeed, CP19, 3:40 Mon Levy, Caleb, CP22, 5:20 Mon Ophelders, Tim, CP47, 4:30 Wed Li, Shi, CP38, 9:00 Wed Seddighin, Saeed, CP27, 9:50 Tue Liu, Paul, CP41, 2:50 Wed P Seemaier, Daniel, CP25, 10:40 Tue Pajor, Thomas, CP17, 2:00 Mon Liu, Sixue, CP29, 2:50 Tue Sering, Leon, CP15, 10:40 Mon Panigrahi, Debmalya, CP3, 10:40 Sun Liu, Tianyu, CP36, 5:45 Tue Shameli, Ali, CP4, 10:40 Sun Pettie, Seth, CP41, 2:00 Wed Liu, Yang, CP43, 3:15 Wed Shi, Elaine, CP40, 9:50 Wed Pilipczuk, Michal, CP20, 3:40 Mon Livni Navon, Inbal R., CP35, 4:55 Tue Siebertz, Sebastian, CP24, 5:45 Mon Plaut, Benjamin, CP32, 3:40 Tue Lodha, Neha, CP21, 4:55 Mon Skoviera, Martin, CP37, 10:15 Wed Polyanskii, Nikita, CP20, 2:25 Mon Luchsinger, Austin, CP44, 3:40 Wed Solomon, Shay, CP31, 2:50 Tue Prusis, Krisjanis, CP28, 10:40 Tue Lyu, Kaifeng, CP2, 9:00 Sun Soma, Tasuku, CP43, 2:25 Wed Q Starikovskaya, Tatiana, CP19, 2:50 Mon M Quanrud, Kent, CP6, 2:50 Sun Sun, Yihan, CP25, 9:00 Tue Mallmann-Trenn, Frederik, CP16, 9:25 Szpankowski, Wojciech, IP1, 11:30 Sun Mon Quanrud, Kent, CP26, 10:40 Tue Mandal, Ritankar, CP7, 3:15 Sun Quanrud, Kent, CP33, 4:30 Tue T Manurangsi, Pasin, CP37, 10:40 Wed R Taki, Setareh, CP41, 3:40 Wed Martinsson, Anders, CP9, 4:30 Sun Raghunathan, Ananth, CP40, 10:15 Tan, Li-Yang, CP11, 4:55 Sun Marx, Dániel, IP2, 11:30 Mon Wed Tan, Zihan, CP24, 4:30 Mon Mastrolilli, Monaldo, CP8, 2:25 Sun Raichel, Benjamin A., CP39, 10:15 Tang, Zhihao Gavin, CP46, 6:10 Wed Meka, Raghu, CP42, 3:40 Wed Wed Tantipongpipat, Uthaipon, CP23, 4:55 Misra, Pranabendu, CP46, 4:30 Wed Rathi, Nidhi, CP32, 3:15 Tue Mon Rathod, Abhishek J., CP44, 2:50 Wed Teng, Yifeng, CP32, 2:25 Tue Reiffenhaeuser, Rebecca, CP32, 2:50 Tengse, Anamay, CP11, 5:20 Sun Tue Tseng, Thomas, CP17, 3:15 Mon Rika, Havana, CP10, 4:30 Sun Rösner, Clemens, CP47, 5:45 Wed Rote, Günter, CP20, 2:00 Mon Conference Program SODA, ALENEX, ANALCO and SOSA 2019 35
V Y Velingker, Ameya, CP45, 4:30 Wed Yogev, Eylon, CP27, 10:15 Tue Vesely, Pavel, CP3, 10:15 Sun Yogev, Eylon, CP27, 10:40 Tue Vincze, Roland, CP28, 10:15 Tue Yoshida, Yuichi, CP43, 2:50 Wed W Yu, Huacheng, CP30, 3:15 Tue Wajc, David, CP37, 9:50 Wed Yuster, Raphael, CP20, 2:50 Mon Wang, Di, CP43, 3:40 Wed Z Wang, Ruosong, CP30, 2:50 Tue Zamir, Or, CP22, 4:55 Mon Wang, Yipu, CP10, 5:20 Sun Zamir, Or, CP29, 3:40 Tue Warode, Philipp, CP15, 10:15 Mon Zehavi, Meirav, CP18, 2:25 Mon Wegrzycki, Karol, CP2, 10:40 Sun Zehavi, Meirav, CP28, 9:50 Tue Wei, Alexander, CP30, 2:00 Tue Zenklusen, Rico, CP26, 9:00 Tue Wei, Hengjia, CP35, 5:45 Tue Zhang, Chihao, CP36, 6:10 Tue Weiß, Armin, CP13, 9:00 Mon Zhang, Fred, CP15, 9:25 Mon Wellnitz, Philip, CP19, 2:25 Mon Zhang, Hengjie, CP14, 10:15 Mon Wild, Sebastian, CP5, 2:00 Sun Zhang, Hongyang, CP12, 5:45 Sun Wlodarczyk, Michal, CP28, 9:25 Tue Zhang, Tianyi, CP31, 3:15 Tue Wu, Ke, CP35, 6:10 Tue Zhong, Mingxian, CP20, 3:15 Mon X Zhong, Peilin, CP45, 6:10 Wed Xie, Tiancheng, CP40, 9:00 Wed Zuo, Song, CP4, 9:00 Sun Xu, Chao, CP33, 4:55 Tue Xu, Jiaming, CP16, 10:40 Mon Xue, Jie, CP7, 2:25 Sun Westin San Diego DA19 Abstracts 1
IP1 and selection problems that involve sets rather than just TowardsAnalysisofInformationContentinDy- individuals. This talk is based on joint work with Sendhil namic Networks Mullainathan, Manish Raghavan, and Maithra Raghu.
Shannon information theory has served as a bedrock for Jon M. Kleinberg advances in communication and storage systems over the Cornell University past six decades. However, this theory does not handle well Dept of Computer Science higher order structures (e.g., graphs, geometric structures), [email protected] temporal aspects (e.g., real-time considerations), or seman- tics, which are essential aspects of data and information that underlie a broad class of current and emerging data IP4 science applications. In this talk, we present some recent results on structural and temporal information in dynamic (Hardness of) Approximation and Expansion networks/graphs, in which nodes and edges are added and removed over time. We focus on two related problems: (i) Recently, there has been some exciting progress towards compression of structures – for a given graph model, we ex- proving the unique games conjecture. Is the conjecture hibit an efficient algorithm for invertibly mapping network true? experts are not sure. Still, it is clearly key to under- structures (i.e., graph isomorphism types) to bit strings of standing approximation algorithms for many optimization minimum expected length, and (ii) node arrival order in- problems, most notably constraint satisfaction. I will de- ference – for a dynamic graph model, we determine the scribe the background and consequences of the conjecture extent to which the order of node arrivals can be inferred and then focus on some interesting technical step pertain- from a snapshot of the graph structure. For both prob- ingtotheexpansion of the so-called Grassman graph. I lems, we apply analytic combinatorics, probabilistic, and will then describe a more general phenomenon called high information-theoretic methods to find statistical limits and dimensional expansion (HDX). HDX is family of a gener- efficient algorithms for achieving those limits. alizations of expansion in graphs to hypergraphs. This is Wojciech Szpankowski a new area of study and has potential for algorithms, as I Purdue University will try to show. Depratment of Computer Science [email protected] Irit Dinur Weizmann Institute [email protected] IP2 Recent Advances on the Complexity of Parameter- ized Counting Problems CP1
Research in the past few decades revealed that a many Ranked Schr¨oder Trees of the basic algorithmic problems are fixed-parameter tractable with various parameterizations: they can be In biology, a phylogenetic tree is a tool to represent the solved in time f(k)nc,wherek is some parameter of the evolutionary relationship between species. Unfortunately, input instance. There have been significant progress both the classical Schr¨oder tree model is not adapted to take into in the design of parameterized algorithms and in under- account the chronology between the branching nodes. In standing the limitations of these techniques. However, up particular, it does not answer the question: how many dif- until recent years, there were relatively few algorithmic ferent phylogenetic stories lead to the creation of n species and complexity results on parameterized counting prob- and what is the average time to get there? In this pa- lems. Similarly to what one experiences in the area of per, we enrich this model in two distinct ways in order to polynomial-time computations, there are natural parame- obtain two ranked tree models for phylogenetics, i.e. mod- terized problems where finding a single solution is fixed- els coding chronology. For that purpose, we first develop parameter tractable, but counting the number of solutions a model of (strongly) increasing Schr¨oder trees, symbol- is hard. In this talk, I will give a survey of recent develop- ically described in the classical context of increasing la- ments, including new results that give a clean and unified beling trees. Then we introduce a generalization for the explanation for the complexity of #k-Path, #k-Matching, labeling with some unusual order constraint in Analytic and many other parameterized counting problems. Combinatorics (namely the weakly increasing trees). Al- though these models are direct extensions of the Schr¨oder D´aniel Marx tree model, it appears that they are also in one-to-one cor- Hungarian Academy of Sciences (MTA SZTAKI), respondence with several classical combinatorial objects. Budapest,Hungary Through the paper, we present these links, exhibit some [email protected] parameters in typical large trees and conclude the studies with efficient uniform samplers. IP3 Antoine Genitrini Inherent Trade-Offs in Algorithmic Fairness Sorbonne University / LIP6 Recent discussion in the public sphere about classification [email protected] by algorithms has involved tension between competing no- tions of what it means for such a classification to be fair Olivier Bodini to different groups. We consider several of the key fairness LIPN, Universit´e Paris 13 conditions that lie at the heart of these debates, and discuss [email protected] recent research establishing inherent trade-offs between these conditions. We also consider a variety of methods Mehdi Naima for promoting fairness and related notions for classification Universit´e Paris-Nord 2 DA19 Abstracts
[email protected] of computer simulations.
Zbigniew Golebiewski, Mateusz Klimczak CP1 Wroclaw University of Science and Technology Reducing Simply Generated Trees by Iterative Leaf [email protected], ma- Cutting [email protected]
We consider a procedure to reduce simply generated trees CP1 by iteratively removing all leaves. In the context of this reduction, we study the number of vertices that are deleted Combinatorics of Nondeterministic Walks of the after applying this procedure a fixed number of times by Dyck and Motzkin Type using an additive tree parameter model combined with a This paper introduces nondeterministic walks,anewvari- recursive characterization. Our results include asymptotic ant of one-dimensional discrete walks. At each step, a formulas for mean and variance of this quantity as well as nondeterministic walk draws a random set of steps from a central limit theorem. a predefined set of sets and explores all possible extensions in parallel. We introduce our new model on Dyck steps Benjamin Hackl with the nondeterministic steps {−1}, {1}, {−1, 1} and Alpen-Adria-Universit¨at Klagenfurt Motzkin steps with the nondeterministic steps {−1}, {0}, [email protected] {1}, {−1, 0}, {−1, 1}, {0, 1}, {−1, 0, 1}. For general lists of step sets and a given length, we express the generating Clemens Heuberger function of nondeterministic walks where at least one of Alpen-Adria-Universit¨at Klagenfurt, Austria the walks explored in parallel is a bridge (ends at the ori- [email protected] gin). In the particular cases of Dyck and Motzkin steps, we also compute the asymptotic probability that at least one Stephan Wagner of those parallel walks is a meander (stays nonnegative) Stellenbosch University, South Africa or an excursion (stays nonnegative and ends at the ori- [email protected] gin). This research is motivated by the study of networks involving encapsulations and decapsulations of protocols. Our results are obtained using generating functions and CP1 analytic combinatorics. Esthetic Numbers and Lifting Restrictions on the Analysis of Summatory Functions of Regular Se- Elie de Panafieu quences Nokia Bell Labs France depanafi[email protected] When asymptotically analysing the summatory function of a q-regular sequence in the sense of Allouche and Shallit, Mohamed Lamine Lamali, Michael Wallner the eigenvalues of the sum of matrices of the linear repre- LaBRI - Universit´e de Bordeaux, France sentation of the sequence determine the “shape’ (in partic- [email protected], wall- ular the growth) of the asymptotic formula. Existing gen- [email protected] eral results for determining the precise behavior (including the Fourier coefficients of the appearing fluctuations) have previously been restricted by a technical condition on these CP2 eigenvalues. The aim of this work is to lift these restric- Fast Modular Subset Sum Using Linear Sketching tions by providing an insightful proof based on generating functions for the main pseudo Tauberian theorem for all Given n positive integers, the Modular Subset Sum prob- cases simultaneously. (This theorem is the key ingredi- lem asks if a subset adds up to a given target t modulo ent for overcoming convergence problems in Mellin–Perron a given integer m. This is a natural generalization of the summation in the asymptotic analysis.) One example is Subset Sum problem (where m =+∞) with ties to additive discussed in more detail: A precise asymptotic formula for combinatorics and cryptography. Recently, in [Bringmann, the amount of esthetic numbers in the first N natural num- SODA’17] and [Koiliaris and Xu, SODA’17], efficient algo- bers is presented. Prior to this only the asymptotic amount rithms have been developed for the non-modular case, run- of these numbers with a given digit-length was known. ning in near-linear pseudo-polynomial time. For the mod- ular case, however, the best known algorithm by Koiliaris Clemens Heuberger,DanielKrenn and Xu [Koiliaris and Xu, SODA’17] runs in time O (m5/4). Alpen-Adria-Universit¨at Klagenfurt, Austria In this paper, we present an algorithm running in time [email protected], [email protected] O (m), which matches a recent conditional lower bound of [Abboud et al.’17] based on the Strong Exponential Time Hypothesis. Interestingly, in contrast to most previous re- CP1 sults on Subset Sum, our algorithm does not use the Fast Protection Number of Recursive Trees Fourier Transform. Instead, it is able to simulate the “text- book’ Dynamic Programming algorithm much faster, using The protection number of a tree is the minimal distance ideas from linear sketching. This is one of the first appli- from its root to a leaf. In this paper we are interested in cations of sketching-based techniques to obtain fast algo- the protection number of a uniformly chosen random recur- rithms for combinatorial problems in an offline setting. sive tree of size n. Due to different construction of plane oriented and non-plane recursive trees we consider them Kyriakos Axiotis separately. We use the singularity analysis of derived gen- Massachusetts Institute of Technology erating functions to find out the number of relevant trees, [email protected] which leads us to the probability distribution of the protec- tion number. Our results are also compared to outcomes Arturs Backurs DA19 Abstracts 3
Toyota Technological Institute at Chicago Ryan Williams [email protected] Massachusetts Institute of Technology [email protected] Ce Jin Institute for Interdisciplinary Information Sciences Tsinghua University CP2 [email protected] Fine-grained Complexity Meets IP = PSPACE
Christos Tzamos In this paper we study the fine-grained complexity of find- University of Wisconsin, Madison ing exact and approximate solutions to problems in P. Our [email protected] main contribution is showing reductions from an exact to an approximate solution for a host of such problems. As one (notable) example, we show that the Closest-LCS- Hongxun Wu Pair problem (Given two sets of strings A and B, com- Institute for Interdisciplinary Information Sciences pute exactly the maximum LCS(a,b) with (a, b) ∈ A × B) Tsinghua University is equivalent to its constant approximation version under [email protected] near-linear time reductions. More generally, we identify a class of problems, which we call BP-SAT-Equivalence- Class, comprising both exact and approximate solutions, CP2 and show that they are all equivalent under near-linear Seth-Based Lower Bounds for Subset Sum and Bi- time reductions. Exploring this class and its properties, we criteria Path also show: 1. Under the NC-SETH assumption (a signifi- cantly more relaxed assumption than SETH), solving any Subset Sum and k-SAT are two of the most extensively of the problems in this class requires essentially quadratic studied problems in computer science, and conjectures time. 2. Modest improvements on the running time of about their hardness are among the cornerstones of fine- known algorithms (shaving log factors) would imply that grained complexity. An important open problem in this NEXP is not in non-uniform NC1. 3. Finally, we leverage area is to base the hardness of one of these problems on our techniques to show new barriers for deterministic ap- the other. Our main result is a tight reduction from k-SAT proximation algorithms for LCS. At the heart of these new to Subset Sum on dense instances, proving that Bellman’s ∗ results is a deep connection between interactive proof sys- 1962 pseudo-polynomial O (T )-time algorithm for Subset tems for bounded-space computations and the fine-grained Sum on n numbers and target T cannot be improved to complexity of exact and approximate solutions to problems − time T 1 ε · 2o(n) for any ε>0, unless the Strong Expo- in P. In particular, our results build on the proof techniques nential Time Hypothesis (SETH) fails. As a corollary, we from the classical IP = PSPACE result. prove a “Direct-OR” theorem for Subset Sum under SETH, offering a new tool for proving conditional lower bounds: Lijie Chen, Shafi Goldwasser It is now possible to assume that deciding whether one out MIT of N given instances of Subset Sum is a YES instance re- [email protected], shafi@theory.csail.mit.edu quires time (NT)1−o(1). As an application of this corollary, we prove a tight SETH-based lower bound for the classi- Kaifeng Lyu cal Bicriteria Path problem, which is extensively studied Tsinghua University in Operations Research. We separate its complexity from [email protected] that of Subset Sum: On graphs with m edges and edge lengths bounded by L, we show that the O(Lm) pseudo- Guy Rothblum polynomial time algorithm by Joksch from 1966 cannot be Microsoft Research improved to O˜(L+m), in contrast to a recent improvement [email protected] for Subset Sum (Bringmann, SODA 2017). Aviad Rubinstein Amir Abboud Harvard University IBM Research Almaden [email protected] [email protected]
Karl Bringmann CP2 Max Planck Institute for Informatics, A Subquadratic Approximation Scheme for Parti- Saarland Informatics Campus, Germany tion [email protected] The subject of this paper is the time complexity of approx- Danny Hermelin, Dvir Shabtay imating Knapsack, Subset Sum, Partition, and some other Ben-Gurion University related problems. The main result is an O(n +1/ε5/3) [email protected], [email protected] time randomized FPTAS for Partition, which is derived from a certain relaxed form of a randomized FPTAS for Subset Sum. To the best of our knowledge, this is the first CP2 NP-hard problem that has been shown to admit a sub- An Equivalence Class for Orthogonal Vectors quadratic time approximation scheme, i.e., one with time complexity of O((n +1/ε)2−δ )forsomeδ>0. To put Abstract not available. these developments in context, note that a quadratic FP- TAS for Partition has been known for 40 years. Our main Lijie Chen contribution lies in designing a mechanism that reduces an MIT instance of Subset Sum to several simpler instances, each [email protected] with some special structure, and keeps track of interactions 4 DA19 Abstracts
between them. This allows us to combine techniques from Microsoft Research approximation algorithms, pseudo-polynomial algorithms, [email protected] and additive combinatorics. We also prove several related results. Notably, we improve approximation schemes for Michael B. Cohen 3SUM, (min,+)-convolution, and Tree Sparsity. Finally, MIT we argue why breaking the quadratic barrier for approxi- [email protected] mate Knapsack is unlikely by giving an Ω((n +1/ε)2−o(1)) conditional lower bound. James R. Lee, Yin Tat Lee University of Washington Marcin Mucha [email protected], [email protected] University of Warsaw Faculty of Mathematics, Informatics and Mechanics [email protected] CP3 k-Servers with a Smile: Online Algorithms via Pro- Karol Wegrzycki,MichalWlodarczyk jections University of Warsaw [email protected], We consider the k-server problem on trees and HSTs. [email protected] We give finite algorithms based on the standard convex- programming primitive of Bregman projections. These al- gorithms have competitive ratios that match some of the CP3 recent results given by Bubeck et al. (STOC 2018), whose A Nearly-Linear Bound for Chasing Nested Convex algorithms were not finite, but were based using mirror- Bodies descent-based continuous dynamics prescribed via a differ- ential inclusion. Friedman and Linial introduced the convex body chas- ing problem to explore the interplay between geometry Marco Molinaro and competitive ratio in metrical task systems. In con- Georgia Institute of Technology vexbodychasing,ateachtimestept ∈ N, the online al- School of Industrial and System Engineering gorithm receives a request in the form of a convex body [email protected] d Kt ⊂ R and must output a point xt ∈ Kt. The goal is to minimize the total movement between consecutive output Niv Buchbinder points, where the distance is measured in some given norm. Statistics and Operations Research Dept. This problem is still far from being understood. Recently d 2 Tel Aviv University, Israel Bansal et al. gave an 6 (d!) -competitive algorithm for [email protected] the nested version, where each convex body is contained within the previous one. We propose a different strategy which is O(d log d)-competitive algorithm for this nested Anupam Gupta convex body chasing problem. Our algorithm works for Carnegie Mellon University any norm. This result is almost tight, given an Ω(d)lower [email protected] bound for the ∞ norm. Joseph Naor C.J. Argue Technion Carnegie Mellon University [email protected] Carnegie Mellon University [email protected] CP3 Sebastien Bubeck Elastic Caching Microsoft Research [email protected] Motivated by applications in cloud computing, we study the classical online caching problem for a cache of variable MichaelB.Cohen size, where the algorithm pays a maintenance cost that MIT monotonically increases with cache size. This captures not [email protected] only the classical setting of a fixed cache size, which cor- responds to a maintenance cost of 0 for a cache of size at most k and ∞ otherwise, but also other natural settings Anupam Gupta in the context of cloud computing such as a concave rental Carnegie Mellon University cost on cache size. We call this the elastic caching problem. [email protected] For this problem, we provide several results: Yin Tat Lee • we give a randomized algorithm with a competitive University of Washington ratio of O(log n); [email protected] • for concave, or more generally submodular mainte- nance costs, we give a deterministic algorithm with a CP3 competitive ratio of 2; Metrical Task Systems on Trees Via Mirror De- • when the cost function is any monotone non-negative scent and Unfair Gluing set function, we give a deterministic n-competitive on- line algorithm; Abstract not available • for the offline version of the problem, we give a ran- Sebastien Bubeck domized constant-factor approximation algorithm. DA19 Abstracts 5
Our algorithms are based on a configuration LP formula- [email protected]ff.cuni.cz tion of the problem, for which our main technical contri- bution is to maintain online a feasible fractional solution that can be converted to an integer solution using existing CP4 rounding techniques. Multi-unit Supply-monotone Auctions with Bayesian Valuations Debmalya Panigrahi Duke University We design multi-unit auctions for budget-constrained bid- [email protected] ders in the Bayesian setting. This extends the work of Dobzinski, Lavi, and Nisan (Games and Economic Behav- Anupam Gupta ior, 2012) and Goel, Mirrokni, and Paes Leme (Games Carnegie Mellon University and Economic Behavior, 2015) from the adversarial to [email protected] the Bayesian setting. Our auctions are supply-monotone, which allows the auction to be run online without know- ing the number of items in advance, and achieve asymp- Ravishankar Krishnaswamy totic revenue-optimality compared to the offline revenue- Microsoft Research India maximizing auction (Pai and Vohra, Journal of Economic [email protected] Theory, 2014). We also give an efficient algorithm for im- plementing our auction by using a succinct and efficiently Amit Kumar implementable characterization of supply-monotonicity in IIT Delhi the Bayesian setting. This provides a generalization of suc- [email protected] cinct characterizations for single unit allocations due to Border (Journal of Econometric Society, 1991; Economic Theory 2007) and Cai, Daskalakis, and Weinberg (STOC, CP3 2012) to the multi-unit setting. A φ-Competitive Algorithm for Scheduling Packets Yuan Deng, Debmalya Panigrahi with Deadlines Duke University [email protected], [email protected] In the online packet scheduling problem with deadlines (PacketScheduling, for short), the goal is to schedule trans- missions of packets that arrive over time in a network CP4 switch and need to be sent across a link. Each packet has Tight Revenue Gaps among Simple Mechanisms a deadline, representing its urgency, and a non-negative weight, that represents its priority. Only one packet can We consider a fundamental problem in microeconomics: be transmitted in any time slot, so, if the system is over- Selling a single item among a number of buyers whose loaded, some packets will inevitably miss their deadlines values are drawn from known independent and regular and be dropped. In this scenario, the natural objective distributions. There are four widely used and stud- is to compute a transmission schedule that maximizes the ied mechanisms in this literature: Anonymous Posted- total weight of packets which are successfully transmitted. Pricing (AP), Second-Price Auction with Anonymous Re- The problem is inherently online, with the scheduling de- serve (AR), Sequential Posted-Pricing (SPM) and Myerson cisions made without the knowledge of future packet ar- Auction (OPT). Myerson Auction is optimal but compli- rivals. The central problem concerning PacketScheduling, cated, which also suffers a few issues in practice such as that has been a subject of intensive study since 2001, is fairness; AP is the simplest mechanism, but its revenue is to determine the optimal competitive ratio of online algo- also the lowest among these four; AR and SPM are of in- rithms, namely the worst-case ratio between the optimum termediate complexity and revenue. We study the revenue total weight of a schedule (computed by an offline algo- gaps among these four mechanisms, which is defined as the rithm) and the weight of a schedule computed by a (de- largest ratio between revenues from two mechanisms. We terministic) online algorithm. We solve this open problem establish two tight ratios and one tighter bound: by presenting a φ-competitive online algorithm for Pack- • etScheduling (where φ ≈ 1.618 is the golden ratio), match- SPM/AP. This ratio studies the power of discrimina- ing the previously established lower bound. tion in pricing schemes. We obtain the tight ratio of roughly 2.62, closing the previous known bounds − Pavel Vesely [e/(e 1),e]. Department of Computer Science, University of Warwick, • AR/AP. This ratio studies the relative power of auc- UK tion vs. pricing schemes, when no discrimination is [email protected]ff.cuni.cz allowed. We get the tight ratio of π2/6 ≈ 1.64, clos- ing the previous known bounds [e/(e − 1),e]. Marek Chrobak • OPT/AR. This ratio studies the power of discrimina- Department of Computer Science tion in auctions. Previously, the revenue gap is known University of California at Riverside, US to be in interval [2,e], and the lower-bound of 2 is con- [email protected] jectured to be tight. We disprove this conjecture by obtaining a better lower-bound of 2.15. Lukasz Jez Tel Aviv University Yaonan Jin University of Wroclaw, Institute of Computer Science The Hong Kong University of Science and Technology [email protected] [email protected]
Jiri Sgall Pinyan Lu Computer Science Institute Shanghai University of Finance and Economics Charles University, Czech Republic [email protected] 6 DA19 Abstracts
Zhihao Gavin Tang with common reserve price, and (ii) when the number of The University of Hong Kong bidders is large, SPM converges to optimum. In addition, [email protected] we extend some results on approximation and computa- tional tractability for lookahead auctions to the correlation- Tao Xiao robust framework. Shanghai Jiao Tong University xt [email protected] Xiaohui Bei Nanyang Technological University [email protected] CP4 Assignment Mechanisms under Distributional Con- Nick Gravin, Pinyan Lu straints Shanghai University of Finance and Economics [email protected], [email protected] We study the assignment problem of objects to agents with heterogeneous preferences under distributional con- Zhihao Gavin Tang straints. Each agent is associated with a publicly known The University of Hong Kong type and has a private ordinal ranking over objects. We [email protected] are interested in assigning as many agents as possible. Our first contribution is a generalization of the well-known and widely used serial dictatorship. Our mechanism maintains CP4 several desirable properties of serial dictatorship, includ- Dynamic Double Auctions: Towards First Best ing strategyproofness, Pareto efficiency, and computational tractability while satisfying the distributional constraints We study the problem of designing dynamic double auc- with a small error. We also propose a generalization of tions for two-sided markets in which a platform intermedi- the probabilistic serial algorithm, which finds an ordinally ates the trade between one seller offering independent items efficient and envy-free assignment, and also satisfies the to multiple buyers, repeatedly over a finite horizon. Mo- distributional constraints with a small error. We show, tivated by online advertising and ride-hailing markets, we however, that no ordinally efficient and envy-free mecha- seek to design mechanisms satisfying the following proper- nism is also weakly strategyproof. Both of our algorithms ties: no positive transfers, i.e., the platform never asks the assign at least the same number of students as the optimum seller to make payments nor buyers are ever paid and peri- fractional assignment. odic individual rationality, i.e., every agent should derive a non-negative utility from every trade opportunity. We pro- Itai Ashlagi vide simple mechanisms satisfying these requirements that Stanford University are asymptotically efficient and budget-balanced with high [email protected] probability as the number of trading opportunities grows. Moreover, we show that the average expected profit ob- Amin Saberi tained by the platform under these mechanisms asymptot- Management Science and Engineering ically approaches first-best (the maximum possible welfare Stanford University generated by the market) either in the case of one buyer [email protected] and one seller, or multiple buyers and one seller with de- terministic values. Ali Shameli Stanford University Santiago Balseiro [email protected] Columbia University [email protected]
CP4 Vahab Mirrokni Correlation-robust Analysis of Single Item Auction Google Inc. [email protected] We investigate the problem of revenue maximization in single-item auction within the new correlation-robust Renato Paes Leme, Song Zuo framework proposed by Carroll [2017] and further devel- Google Research oped by Gravin and Lu [2018]. In this framework the auc- [email protected], [email protected] tioneer is assumed to have only partial information about marginal distributions, but does not know the dependency structure of the joint distribution. The auctioneer’s rev- CP5 enue is evaluated in the worst-case over the uncertainty QuickSort: Improved Right-Tail Asymptotics for of possible joint distribution. For the problem of opti- the Limiting Distribution, and Large Deviations mal auction design in the correlation robust-framework we observe that in most cases the optimal auction does not We refine asymptotic logarithmic upper bounds – extend- admit a simple form like the celebrated Myerson’s auc- ing from one term to three – produced by Svante Janson tion for independent valuations. We analyze and com- (2015) on the right tail of the limiting QuickSort distri- pare performances of several DSIC mechanisms used in bution function F and by Fill and Hung (2018) on the practice. Our main set of results concern the sequen- right tails of the corresponding density f and of the abso- tial posted-price mechanism (SPM). We show that SPM lute derivatives of f of each order. All our results match achieves a constant (4.78) approximation to the optimal two-term lower bounds for the functions in question to correlation-robust mechanism. We also show that in the two terms and match conjectured asymptotic expansions symmetric (anonymous) case when all bidders have the to three terms. Using the refined asymptotic bounds on F , same marginal distribution, (i) SPM has almost match- we derive right-tail large deviation (LD) results for the dis- ing worst-correlation revenue as any second price auction tribution of the number of comparisons required by Quick- DA19 Abstracts 7
Sort that sharpen somewhat the two-sided LD results of eral pivots reduces overall memory transfers, we have seen McDiarmid and Hayward (1996). renewed interest in multiway Quicksort. Here, we ana- lyze in how far multiway partitioning helps in Quickse- James Allen Fill, Wei-Chun Hung lect. We compute the expected number of comparisons Johns Hopkins University and scanned elements (approximating memory transfers) jimfi[email protected], [email protected] for a generic class of (non-adaptive) multiway Quickselect and show that three or more pivots are not helpful, but two pivots are. Moreover, we consider “adaptive’ variants CP5 which choose partitioning and pivot-selection methods in Moments of Select Sets each recursive step from a finite set of alternatives depend- ing on the current (relative) sought rank. We show that We analyze a selection procedure introduced by Krieger, “Sesquickselect’, a new Quickselect variant that uses either Pollak, and Samuel–Cahn. It retains an item if it is among one or two pivots, makes better use of small samples w.r.t. the top 100p percent, as compared to the items that have memory transfers than other Quickselect variants. been accepted so far. Gaither and Ward analyzed the aver- age behavior of the number of items selected. We present the asymptotic properties of the higher moments of the Sebastian Wild number of items retained by the selection procedure. We David R. Cheriton School of Computer Science derive a general formula for the moments. To demonstrate University of Waterloo the complexity of these moments, we present the exact [email protected] first-order asymptotic growth of some of these moments, for various rational values of p. Conrado Mart´ınez Department of Computer Science Simon H. Langowski, Mark Daniel Ward Univ. Polit`ecnica de Catalunya Purdue University [email protected] Department of Statistics [email protected], [email protected] Markus Nebel Universit¨at Bielefeld [email protected] CP5 Median-of-K Jumplists and Dangling-Min BSTs
We extend randomized jumplists introduced by CP6 Br¨onnimann et al. (STACS 2003) to choose jump- pointer targets as median of a small sample for better Stochastic Submodular Cover with Limited Adap- search costs, and present randomized algorithms with tivity expected O(log n) time complexity that maintain the prob- ability distribution of jump pointers upon insertions and In the submodular cover problem, we are given a non- deletions. We analyze the expected costs to search, insert negative monotone submodular function f over a ground and delete a random element, and we show that omitting set E of items, and the goal is to choose a smallest sub- jump pointers in small sublists hardly affects search costs, set S ⊆ E such that f(S)=Q where Q = f(E). In the but significantly reduces the memory consumption. We stochastic version of the problem, we are given m stochas- use a bijection between jumplists and “dangling-min tic items which are different random variables that inde- BSTs’, a variant of (fringe-balanced) binary search trees pendently realize to some item in E, and the goal is to for the analysis. Despite their similarities, some standard find a smallest set of stochastic items whose realization R analysis techniques for search trees fail for dangling-min satisfies f(R)=Q. The problem captures as a special trees (and hence for jumplists). case the stochastic set cover problem and more generally, stochastic covering integer programs. We study the role Sebastian Wild of adaptivity in the stochastic submodular cover problem. David R. Cheriton School of Computer Science We define an r-round adaptive algorithm to be an algo- University of Waterloo rithm that chooses a permutation of all available items in [email protected] each round k ∈ [r], and a threshold τk, and realizes items in the order specified by the permutation until the func- Markus Nebel tion value exceeds τk. Our main result is that for any Universit¨at Bielefeld integer r, there exists a poly-time r-round adaptive algo- [email protected] rithm for stochastic submodular cover whose expected cost is O˜(Q1/r) times the expected cost of a fully adaptive algo- Elisabeth Neumann rithm. We complement this result by showing that for any Carl-Friedrich-Gauß-Fakult¨at r, there exist instances of the stochastic submodular cover Technische Universit¨at Braunschweig, Germany problem where no r-round adaptive algorithm can achieve [email protected] better than Ω(Q1/r) approximation to the expected cost of a fully adaptive algorithm. CP5 Sesquickselect: One and a Half Pivots for Cache- Arpit Agarwal Efficient Selection PhD Student University of Pennsylvania Because of unmatched improvements in CPU performance, [email protected] memory transfers have become a bottleneck of program execution. As discovered in recent years, this also affects Sepehr Assadi, Sanjeev Khanna sorting in internal memory. Since partitioning around sev- University of Pennsylvania 8 DA19 Abstracts
[email protected], [email protected] oracle in expectation. The approximation guarantee and query complexity are optimal, and the adaptivity is nearly optimal. Moreover, the number of queries is substantially CP6 less than in previous works. Last, we extend our results to An Exponential Speedup in Parallel Running Time the submodular cover problem to demonstrate the gener- for Submodular Maximization Without Loss in Ap- ality of our algorithm and techniques. proximation Matthew Fahrbach Abstract not available Georgia Institute of Technology [email protected] Eric Balkanski, Aviad Rubinstein, Yaron Singer Harvard University Vahab Mirrokni [email protected], [email protected], Google Inc. [email protected] [email protected]
Morteza Zadimoghaddam CP6 Google Submodular Maximization with Nearly-optimal [email protected] Approximation and Adaptivity in Nearly-linear Time CP6 In this paper, we study the tradeoff between the approxi- Deterministic (1/2 + )-Approximation for Sub- mation guarantee and adaptivity for the problem of maxi- modular Maximization over a Matroid mizing a monotone submodular function subject to a car- dinality constraint. The adaptivity of an algorithm is the In this talk, we will consider the problem of maximizing a number of sequential rounds of queries it makes to the monotone submodular function subject to a matroid con- evaluation oracle of the function, where in every round straint and present a deterministic algorithm that achieves the algorithm is allowed to make polynomially-many par- (1/2 + )-approximation for the problem. This algorithm allel queries. Adaptivity is an important consideration in is the first deterministic algorithm known to improve over settings where the objective function is estimated using the 1/2-approximation ratio of the classical greedy algo- samples and in applications where adaptivity is the main rithm proved by Nemhauser, Wolsely and Fisher in 1978. running time bottleneck. Previous algorithms achieving anearly-optimal1− 1/e − approximation require Ω(n) rounds of adaptivity. In this work, we give the first al- Niv Buchbinder gorithm that achieves a 1 − 1/e − approximation using Statistics and Operations Research Dept. O(ln n/2) rounds of adaptivity. The number of function Tel Aviv University, Israel evaluations and additional running time of the algorithm [email protected] are O(n poly(log n, 1/)). Moran Feldman, Mohit Garg Alina Ene Open University of Israel Boston University [email protected], [email protected] [email protected]
Huy Nguyen CP6 Northeastern University Stochastic p Load Balancing and Moment Prob- [email protected] lems via the L-Function Method
This paper considers stochastic optimization problems CP6 whose objective functions involve powers of random vari- Submodular Maximization with Nearly Optimal ables. For example, consider the classic Stochastic lp Load Approximation, Adaptivity and Query Complexity Balancing Problem (SLBp): There are m machines and n jobs, and known independent random variables Yij de- Submodular optimization generalizes many classic prob- cribe the load incurred on machine i if we assign job j to lems in combinatorial optimization and has recently found it. The goal is to assign each jobs to machines in order to a wide range of applications in machine learning (e.g., fea- minimize the expected lp-norm of the total load on the ma- ture engineering and active learning). For many large-scale chines. While convex relaxations represent one of the most optimization problems, we are often concerned with the powerful algorithmic tools, in problems such as SLBp the adaptivity complexity of an algorithm, which quantifies the main difficulty is to capture the objective function in a way number of sequential rounds where polynomially-many in- that only depends on each random variable separately. We dependent function evaluations can be executed in paral- show how to capture p-power-type objectives in such sep- lel. While low adaptivity is ideal, it is not sufficient for a arable way by using the L-function method, introduced by distributed algorithm to be efficient, since in many practi- Latala to relate the moment of sums of random variables to cal applications of submodular optimization the number the individual marginals. We show how this quickly leads of function evaluations becomes prohibitively expensive. to a constant-factor approximation for very general subset Motivated by these applications, we study the adaptivity selection problem with p-moment objective. Moreover, we and query complexity of adaptive submodular optimiza- give a constant-factor approximation for SLBp, improv- tion. Our main result is a distributed algorithm for max- ing on the recent O(p/ ln p)-approximation of [Gupta et imizing a monotone submodular function with cardinality al., SODA 18]. Here the application of the method is constraint k that achieves a (1 − 1/e − ε)-approximation much more involved. In particular, we need to sharply in expectation. This algorithm runs in O(log(n)) adaptive connect the expected lp-norm of a random vector with the rounds and makes O(n) calls to the function evaluation p-moments of its marginals (machine loads), taking into ac- DA19 Abstracts 9
count simultaneously the different scales of the loads that The Hong Kong University of Science and Technology are incurred by an unknown assignment. [email protected]
Marco Molinaro Guilherme D. da Fonseca Georgia Institute of Technology Universit´e Clermont Auvergne, LIMOS School of Industrial and System Engineering France [email protected] [email protected]
CP6 David M. Mount University of Maryland Submodular Function Maximization in Parallel via [email protected] the Multilinear Extension
Balkanski and Singer [5] recently initiated the study of adaptivity (or parallelism) for constrained submodular CP7 function maximization, and studied the setting of a car- New Lower Bounds for the Number of Pseudoline dinality constraint. Very recent improvements for this Arrangements problem by Balkanski, Rubinstein, and Singer [6] and Ene and Nguyen [21] resulted in a near-optimal (1 − 1/e − )- Arrangements of lines and pseudolines are fundamental ob- approximation in O(log n/2) rounds of adaptivity. Partly jects in discrete and computational geometry. They also motivated by the goal of extending these results to more appear in other areas of computer science, such as the general constraints, we describe parallel algorithms for ap- study of sorting networks. Let Bn be the number of ar- proximately maximizing the multilinear relaxation of a rangements of n pseudolines and let bn =log2 Bn.The monotone submodular function subject to packing con- problem of estimating Bn was posed by Knuth in 1992. ≤ n 2 straints. Formally our problem is to maximize F (x)over Knuth conjectured that bn 2 + o(n ) and also derived ∈ n ≤ 2 x [0, 1] subject to Ax 1whereF is the multilinear the first upper and lower bounds: bn ≤ 0.7924(n + n) 2 relaxation of a monotone submodular function. Our algo- and bn ≥ n /6 − O(n). The upper bound underwent sev- − − 2 rithm achieves a near-optimal (1 1/e )-approximation eral improvements, bn ≤ 0.6988n (Felsner, 1997), and 2 4 2 in O(log m log n/ ) rounds where n is the cardinality of bn ≤ 0.6571n (Felsner and Valtr, 2011), for large n.Here 2 the ground set and m is the number of packing constraints. we show that bn ≥ cn − O(n log n)forsomeconstant 2 For many constraints of interest, the resulting fractional c>0.2053. In particular, bn ≥ 0.2053 n for large n.This 2 solution can be rounded via known randomized rounding improves the previous best lower bound, bn ≥ 0.1887n , schemes that are oblivious to the specific submodular func- due to Felsner and Valtr (2011). Our arguments are ele- tion. We thus derive randomized algorithms with poly- mentary and geometric in nature. Further, our construc- logarithmic adaptivity for a number of constraints includ- tions are likely to spur new developments and improved ing partition and laminar matroids, matchings, knapsack lower bounds for related problems, such as in topological constraints, and their intersections. graph drawings.
Chandra Chekuri, Kent Quanrud Adrian Dumitrescu University of Illinois at Urbana-Champaign Department of Computer Science [email protected], [email protected] University of Wisconsin-Milwaukee [email protected] CP7 Ritankar Mandal Approximate Nearest Neighbor Searching with University of Wisconsin–Milwaukee, USA Non-euclidean and Weighted Distances [email protected] We present a new approach to ε-approximate nearest- neighbor searching in Rd for fixed d under a variety of dis- tance functions including the Bregman divergence, the Ma- CP7 halanobis distance, the Minkowski metric, multiplicative Optimal Algorithm for Geodesic Nearest-point weights, and convex scaling distance functions. The most Voronoi Diagrams in Simple Polygons efficient data structure previously known for the Euclidean metric resisted generalization because of its reliance on the Given a set of m point sites in a simple polygon, the lifting transformation, and the best known approaches for geodesic nearest-point Voronoi diagram of the sites par- other distance functions are far less efficient. We circum- titions the polygon into m Voronoi cells, one cell per site, vent the reliance on the lifting transformation by a careful such that every point in a cell has the same nearest site application of convexification. While this technique is well under the geodesic metric. In this paper, we present an known in the field of non-linear optimization, it appears to O(n + m log m)-time algorithm for computing the geodesic be relatively new to computational geometry. Under mild nearest-point Voronoi diagram of m points in a simple n- assumptions on the distance functions in question, the pro- gon. This matches the best known lower bound of Ω(n + posed data structures answer queries in logarithmic time m log m) as well as improving the previously best known with storage O(n log(1/ε)/εd/2), where n is the number of algorithms which take time O(n + m log m + m log2 n)and data points. This nearly matches the best known results O(n log n + m log m). This answers the longstanding ques- for the Euclidean metric. tion whether the geodesic nearest-point Voronoi diagram can be computed optimally, which was explicitly posed by Ahmed Abdelkader Aronov [Algorithmica, 1989] and Mitchell [Handbook of University of Maryland Computational Geometry, 2000]. [email protected] Eunjin Oh Sunil Arya Max Planck Institute for Informatics 10 DA19 Abstracts
[email protected] sign efficient (1 + ε)-approximate CRCP data structures for orthogonal queries in the plane, where ε>0isapre- specified parameter. The highlights are two data struc- CP7 tures for answering rectangle queries, one of which uses −1 4 4 −1 3 −2 Extremal and Probabilistic Results for Order O(ε n log n)spaceandO(log n + ε log n + ε log n) −1 3 Types query time while the other uses O(ε n log n)spaceand O(log5 n + ε−1 log4 n + ε−2 log2 n) query time. In addi- A configuration is a finite set of points in the plane. Two tion, we also apply our techniques to the CRCP problem configurations A and B have the same order type if there in higher dimensions, obtaining efficient data structures for exists a bijection between them preserving the orienta- slab, 2-box, and 3D dominance queries. tion of every ordered triple. We investigate extremal and probabilistic problems related to configurations in general Jie Xue position. We focus on problems involving forbidden con- University of Minnesota, Twin Cities figurations or monotone/hereditary properties. Given a [email protected] configuration B, we consider the property of being “B- free’: a configuration A is B-free if no subset of points of A has the same order type as B. We prove a signifi- CP8 cant bound on the number of B-free N-point configurations An Algorithmic Blend of LPs and Ring Equations containedinthem × m grid [m]2 for arbitrary configura- for Promise CSPs tions B. We consider random N-point configurations U in the unit square, in which each of the N points is cho- Promise CSPs are a relaxation of constraint satisfaction sen uniformly at random and independently of all other problems where the goal is to find an assignment satisfying points. The above-mentioned enumeration result for B- a relaxed version of the constraints. Promise CSPs in- free configurations in the grid is then used to prove strong clude approximate graph coloring, discrepancy minimiza- bounds for the probability that the random set U should be tion, and interesting variants of satisfiability. Similar to B-free for any given B. We also investigate the threshold CSPs, the tractability of promise CSPs can be tied to the function N0 = N0(n) for the property that U should be n- structure of operations on the solution space called poly- universal, that is, should contain all n-point configurations morphisms, though in the promise world these operations in general position. As it turns out, N0 = N0(n) is doubly are much less constrained. In previous work, we classified exponential in n. Our arguments are mostly geometric and Boolean promise CSPs when the constraint predicates are combinatorial, with the recent container method playing an symmetric. In this work, we vastly generalize these algo- important role. rithmic results. Specifically, we show that promise CSPs that admit a family of ”regional-periodic” polymorphisms Jie Han are in P, assuming that determining which region a point University of Rhode Island is in can be computed in polynomial time. Our algorithm jie [email protected] is based on a novel combination of linear programming and solving linear systems over rings. We also abstract a frame- Yoshiharu Kohayakawa work based on reducing a promise CSP to a CSP over an Instituto de Matematica e Estatistica infinite domain, solving it there, and then rounding the Universidade de S˜ao Paulo, Brazil solution to an assignment for the promise CSP instance. [email protected] The rounding step is intimately tied to the family of poly- morphisms and clarifies the connection between polymor- phisms and algorithms in this context. As a key ingredient, Marcelo Sales we introduce the technique of finding a solution to an LP Emory University with integer coefficients that lies in a different ring to by- [email protected] pass ad-hoc adjustments for lying on a rounding boundary.
Henrique Stagni Joshua Brakensiek, Venkatesan Guruswami University of Sao Paulo Carnegie Mellon University [email protected] [email protected], [email protected]
CP7 CP8 Colored Range Closest-Pair Problem under Gen- Perfect Matchings, Rank of Connection Tensors eral Distance Functions and Graph Homomorphisms
The range closest-pair (RCP) problem is the range-search We develop a theory of graph algebras over general fields. version of the classical closest-pair problem, which aims This is modeled after the theory developed by Freed- to store a given dataset of points in some data struc- man, Lov´asz and Schrijver in ”[Michael Freedman, L´aszl´o ture such that when a query range X is specified, the Lov´asz, Alexander Schrijver, Reflection positivity, rank closest pair of points contained in X can be reported ef- connectivity, and homomorphism of graphs]” for connec- ficiently. A natural generalization of the RCP problem tion matrices, in the study of graph homomorphism func- is the colored RCP (CRCP) problem in which the given tions over real edge weight and positive vertex weight. We data points are colored and the goal is to find the clos- introduce connection tensors for graph properties. This est bichromatic pair contained in the query range. All notion naturally generalizes the concept of connection ma- the previous work on the RCP problem was restricted to trices. It is shown that counting perfect matchings, and a the uncolored version and the Euclidean distance function. host of other graph properties naturally defined as Holant In this paper, we make the first progress on the CRCP problems (edge models), cannot be expressed by graph ho- problem. We investigate the problem under a general dis- momorphism functions over the complex numbers (or even tance function induced by a monotone norm; this covers more general fields). Our necessary and sufficient condi- all Lp-metrics for p>0andtheL∞-metric. We de- tion in terms of connection tensors is a simple exponential DA19 Abstracts 11
rank bound. It shows that positive semidefiniteness is not IDSIA, University of Lugano needed in the more general setting. [email protected]
Jin-Yi Cai University of Wisconsin, Madison CP8 [email protected] Fast Greedy for Linear Matroids
Artsiom Hovarau A fundamental algorithmic result for matroids is that the University of Wisconsin-Madison maximum weight base can be computed using the greedy [email protected] algorithm. For explicitly represented matroids an impor- tant question is the time complexity of computing such a base. It is known that one can compute it in time almost CP8 linear in the number of non-zero entries of the linear rep- Probabilistic Tensors and Opportunistic Boolean resentation plus rω,wherer is the rank of the matroid and Matrix Multiplication ω is the matrix multiplication exponent. In this work, we give an alternative algorithm for the same task. We introduce probabilistic extensions of classical determin- istic measures of algebraic complexity of a tensor, such Huy L. Nguyen as the rank and the border rank. We show that these Northeastern University probabilistic extensions satisfy various natural and algo- [email protected] rithmically serendipitous properties, such as submultiplica- tivity under taking of Kronecker products. Furthermore, the probabilistic extensions enable improvements over their CP9 deterministic counterparts for specific tensors of interest, Subcritical Random Hypergraphs, High-Order starting from the tensor 2, 2, 2 that represents 2×2matrix Components, and Hypertrees multiplication. While it is well known that the (determin- istic) tensor rank and border rank satisfy In the binomial random graph G(n, p), when p changes from (1−ε)/n (subcritical case) to 1/n andthento(1+ε)/n rk 2, 2, 2 =7 and rk 2, 2, 2 =7 (supercritical case) for ε>0, with high probability the [V. Strassen, Numer. Math. 13 (1969); J. E. Hopcroft and order of the largest component increases smoothly from L. R. Kerr, SIAM J. Appl. Math. 20 (1971); S. Winograd, O(ε−2 log(ε3n)) to Θ(n2/3) and then to (1 ± o(1))2εn.As Linear Algebra Appl. 4 (1971); J. M. Landsberg, J. AMS a natural generalisation of random graphs and connected- 19 (2006)], we show that the probabilistic tensor rank and ness, we consider the binomial random k-uniform hyper- border rank satisfy graph H k(n, p)(whereeachk-tupleofverticesispresent 6 2 as a hyperedge with probability p independently) and the rk 2, 2, 2 ≤6+ and rk 2, 2, 2 ≤6+ . 7 3 following notion of high-order connectedness. Given an integer 1 ≤ j ≤ k − 1, two sets of j vertices are called By submultiplicativity, this leads immediately to novel ran- j-connected if there is a walk of hyperedges between them domized algorithm designs, such as algorithms for Boolean such that any two consecutive hyperedges intersect in at matrix multiplication as well as detecting and estimating least j vertices. A j-connected component is a maximal the number of triangles and other subgraphs in graphs. collection of pairwise j-connected j-tuples of vertices. Re- cently, the threshold for the appearance of the giant j- Petteri Kaski, Matti Karppa k Aalto University connected component in H (n, p) and its order were deter- Department of Computer Science mined. In this article, we take a closer look at the subcrit- petteri.kaski@aalto.fi, matti.karppa@aalto.fi ical random hypergraph. We determine the structure and size (i.e. number of hyperedges) of the largest j-connected components, with the help of a certain class of ‘hypertrees’ CP8 and related objects. In our proofs, we combine various The Complexity of the Ideal Membership Problem probabilistic and enumerative techniques, such as generat- for Constrained Problems Over the Boolean Do- ing functions and couplings with branching processes. main Wenjie Fang, Oliver Cooley, Nicola Del Giudice Given an ideal I and a polynomial f the Ideal Membership Graz University of Technology Problem is to test if f ∈ I. This problem is a funda- [email protected], [email protected], mental algorithmic problem with important applications [email protected] and notoriously intractable. We study the complexity of the Ideal Membership Problem for combinatorial ideals Mihyun Kang that arise from constrained problems over the Boolean do- Technische Universitaet Graz main. As our main result, we identify the precise border- Institut fuer Optimierung und Diskrete Mathematik line of tractability. By using Gr¨obner bases techniques, [email protected] we generalize Schaefer’s dichotomy theorem [STOC, 1978] which classifies all Constraint Satisfaction Problems over the Boolean domain to be either in P or NP-hard. This CP9 paper is motivated by the pursuit of understanding the Degree Distributions of Generalized Hooking Net- recently raised issue of bit complexity of Sum-of-Squares works proofs [O’Donnell, ITCS, 2017]. Raghavendra and Weitz [ICALP, 2017] show how the Ideal Membership Problem A hooking network is grown from a set of graphs called tractability for combinatorial ideals implies bounded coef- blocks, each block with a labelled vertex called a hook. At ficients in Sum-of-Squares proofs. each step in the growth of the network, a vertex called a latch is chosen from the hooking network, and a block is Monaldo Mastrolilli attached by joining the hook of the block with the latch. 12 DA19 Abstracts
These graphs generalize trees, which are hooking networks randomly chosen hyperedges satisfy an appropriate notion grown from a single edge as the only block. Using P´olya limited independence. We leave several open problems re- urns, we show multivariate normal limit laws for the degree lated to these quasi-random hypergraphs and the thresh- distributions of hooking networks. We extend previous re- olds of associated problem variations. sults by allowing for more than one block in the growth of the network and by studying arbitrarily large degrees. Michael Mitzenmacher Harvard University Colin Desmarais [email protected] Uppsala University [email protected] CP9 Cecilia Holmgren Random Walks on Graphs: New Bounds on Hit- Uppsala University ting, Meeting, Coalescing and Returning Department of Mathematics [email protected] We prove new results on lazy random walks on finite graphs. To start, we obtain new estimates on return prob- abilities P t(x, x) and the maximum expected hitting time CP9 thit, both in terms of the relaxation time. We also prove When Does Hillclimbing Fail on Monotone Func- a discrete-time version of the first-named author’s “Meet- tions? ing time lemma” that bounds the probability of a random walk hitting a deterministic trajectory in terms of hitting Hillclimbing is an essential part of optimization. An im- times of static vertices. The meeting time result is then portant benchmark for hillclimbing algorithms on functions used to bound the expected full coalescence time of mul- f : {0, 1}n → R are (strictly) montone functions, on which tiple random walks over a graph. This last theorem is a a surprising number of hillclimbers fail to be efficient. For discrete-time version of a result by the first-named author, example, the (1 + 1)-Evolutionary Algorithm is a standard which had been previously conjectured by Aldous and Fill. hillclimber which flips each bit independently with prob- Our bounds improve on recent results by Lyons and Oveis- ability c/n in each round. Perhaps surprisingly, this al- Gharan; Kanade et al; and (in certain regimes) Cooper et gorithm shows a phase transition: it optimizes any such al. monotone function in quasilinear time if c<1, while, on the other hand, it is known how to construct monotone Roberto Oliveira functions for which the algorithm needs exponential time IMPA, Rio de Janeiro if c is sufficiently large. As the previsouly known argument [email protected] for quasilinear running time breaks down at c =1,itis natural to suspect that the phase transition should occur Yuval Peres at this value. We here present a new runtime analysis for Microsoft Research, Redmond the (1 + 1)-EA which shows that this is not the case. More [email protected] precisely, there is a c0 > 1 such that the running time of the algorithm remains quasilinear for c matching) in Kh-minor free graphs with h = O(1) and [email protected] integer edge weights having magnitude at most C.This improves upon the O˜(n10/7 log C) algorithm of Cohen et al. [SODA 2017] and the O(n3/2 log(nC)) algorithm of CP10 Gabow and Tarjan [SIAM J. Comput. 1989]. For a graph On Constant Multi-Commodity Flow-Cut Gaps for with m edges and n vertices, the Hopcroft-Karp [SIAM J. Families of Directed Minor-Free Graphs Comput. 1973], and Gabow-Tarjan algorithms compute, in each phase, a maximal set of vertex-disjoint shortest aug- The multi-commodity flow-cut gap is a fundamental pa- menting paths (for appropriately defined costs) in O(m) rameter that affects the performance of several divide & time.√ These algorithms arrive at an optimal matching√ af- conquer algorithms, and has been extensively studied for ter O( n) phases with a total execution time of O(m n). various classes of undirected graphs. It has been shown To obtain our speed-up, we relax the conditions on the by Linial, London and Rabinovich [1994] and by Aumann augmenting paths and compute, in each phase, a set of and Rabani [1998] that for general n-vertex graphs it is augmenting paths that are not restricted to be shortest or bounded by O(log n) and the Gupta-Newman-Rabinovich- vertex-disjoint. As a result, our algorithm computes sub- Sinclair conjecture [2004] asserts that it is O(1) for any stantially more augmenting paths√ in each phase, reducing family of graphs that excludes some fixed minor. The the number of phases from O( n)toO(n2/5). By using flow-cut gap is poorly understood for the case of directed small vertex separators, the execution of each phase takes graphs. We show that for uniform demands it is O(1) on O˜(m) time on average. For planar graphs, we combine our directed series-parallel graphs, and on directed graphs of algorithm with efficient shortest path data structures to ob- bounded pathwidth. These are the first constant upper tain a minimum-cost perfect matching in O˜(n6/5 log (nC)) bounds of this type for some non-trivial family of directed graphs. We also obtain O(1) upper bounds for the general time. This improves upon the recent O˜(n4/3 log (nC)) time multi-commodity flow-cut gap on directed trees and cycles. algorithm by Asathulla et al. [SODA 2018]. These bounds are obtained via new embeddings and Lip- schitz quasipartitions for quasimetric spaces, which gener- Nathaniel Lahn, Sharath Raghvendra alize analogous results form the metric case, and could be Virginia Tech of independent interest. Finally, we discuss limitations of [email protected], [email protected] methods that were developed for undirected graphs, such as random partitions, and random embeddings. Ario Salmasi CP10 The Ohio State University [email protected] Flow-Cut Gaps and Face Covers in Planar Graphs Anastasios Sidiropoulos University of Illinois at Chicago The relationship between the sparsest cut and the maxi- [email protected] mum concurrent multi-flow in graphs has been studied ex- tensively. For general graphs with k terminal pairs, the flow-cut gap is O(log k), and this is tight. For planar Vijay Sridhar graphs, it has been conjectured that their flow-cut gap is Mathworks [email protected] O(1), while the known bounds place the gap somewhere√ between 2 (Lee and Raghavendra, 2003) and O( log k) (Rao, 1999). Okamura and Seymour (1981) show that CP10 when all the terminals of a planar network lie on a sin- gle face, the flow-cut gap is exactly 1. This setting can Maximum Integer Flows in Directed Planar be generalized by considering planar networks where the Graphs with Vertex Capacities and Multiple terminals lie on γ>1 faces in some fixed planar draw- Sources and Sinks ing. Lee and Sidiropoulos (2009) proved that the flow-cut gap is bounded by a function of γ, and Chekuri, Shep- We consider the problem of finding maximum flows in pla- herd, and Weibel (2013) showed that the gap is at most nar graphs with capacities on both vertices and arcs and 3γ. We prove that the flow-cut gap is O(log γ), by show- with multiple sources and sinks. We present three algo- ing that the edge-weighted shortest-path metric induced on rithms when the capacities are integers. The first algo- 3 the terminals admits a stochastic embedding into trees with rithm runs in O(n log n + kn) time when all capacities are distortion O(log γ), which is tight. For vertex-capacitated bounded, where n is the number of vertices in the graph networks, Lee, Mendel, and Moharrami (2015) showed that and k is the number of terminals. This algorithm is the first the vertex-capacitated flow-cut gap is O(1) on planar net- to solve the vertex-disjoint paths problem in near-linear works whose terminals lie on a single face. We prove that time when k is bounded but larger than 2. The second 5 the flow-cut gap is O(γ) for vertex-capacitated instances algorithm runs in O(k n polylog(nU)) time, where U is when the terminals lie on γ>1 faces. In fact, this result the largest finite capacity of a single vertex. Finally, when holds in the more general setting of submodular vertex ca- k = 3, we present an algorithm that runs in O(n log n) pacities. time; this algorithm works even when the capacities are arbitrary reals. Our algorithms improve on the fastest previously known algorithms when k is bounded and U Havana Rika, Robert Krauthgamer is bounded by a polynomial in n. Prior to this result, the Weizmann Institute of Science fastest algorithms ran in O(n2/ log n) time for real capaci- [email protected], ties, O(n3/2 log n log U) for integer capacities, and O˜(n10/7) [email protected] for unit capacities, even when k =3. James R. Lee Yipu Wang University of Washington University of Illinois at Urbana-Champaign 14 DA19 Abstracts [email protected] such graphs in log-space. Yang Liu CP11 Stanford University Quantum Algorithms and Approximating Polyno- [email protected] mials for Composed Functions with Shared Inputs Ofer Grossman We give faster quantum algorithms for evaluating com- Berkeley posed functions whose inputs may be shared between [email protected] bottom-level gates. Let f be a Boolean function and con- sider a function F obtained by applying f to conjunc- tions of possibly overlapping subsets of n variables. If CP11 f has quantum query complexity Q (f), we give an al- A Deterministic PTAS for the Algebraic Rank of gorithm for evaluating F using O˜( Q(f) · n)√ quantum Bounded Degree Polynomials queries. This improves on the bound of O(Q(f) · n)that follows by treating each conjunction independently, and is We consider the problem of computing the algebraic rank tight for worst-case choices of f. Using completely different of a given set of multivariate polynomials over a field of techniques, we prove a similar tight composition theorem characteristic zero. The notion of algebraic rank natu- for the approximate degree of f. By recursively apply- rally generalizes the notion of rank in linear algebra. The ing our composition theorems, we obtain a nearly optimal decision version of the problem is equivalent to the cel- − −d ebrated Polynomial Identity Testing (PIT) problem. A O˜(n1 2 ) upper bound on the quantum query complexity randomized polynomial time algorithm for this problem is and approximate degree of linear-size depth-d AC0 circuits. known thanks to the classical Jacobian criterion, and the As a consequence, such circuits can be PAC learned in Schwartz-Zippel lemma. However, no deterministic algo- subexponential time, even in the challenging agnostic set- rithm is known. In fact, a deterministic algorithm would ting. Prior to our work, a subexponential-time algorithm imply a deterministic algorithm for the PIT problem which was not known even for linear-size depth-3 AC0 circuits. itself would imply circuit complexity lower bounds (Ka- We also show that any substantially faster learning algo- banets, Impagliazzo, STOC’03). We present a determin- rithm will require fundamentally new techniques. istic polynomial time approximation scheme (PTAS) for Mark Bun computing the algebraic rank of a set of bounded degree Simons Institute for the Theory of Computing polynomials. More specifically, we give an algorithm that { }⊂F [email protected] takes as input a set f := f1,...,fn [x1,...,xm]of polynomials with degrees bounded by d, and a rational d2 nmd O( ) · Robin Kothari number >0 and runs for time O(( ) M(n)), Microsoft Research where M(n) is the time required to compute the rank of [email protected] an n × n matrix (with field entries), and finally outputs a number r, such that r is at least (1 − ) times the algebraic Justin Thaler rank of f. Georgetown University Vishwas Bhargava [email protected] Rutgers University [email protected] CP11 Markus Bl¨aser Reproducibility and Pseudo-determinism in Log- Saarland University space [email protected] A curious property of randomized log-space search algo- rithms is that their outputs are often longer than their Gorav Jindal workspace. This leads to the question: how can we repro- Aalto University duce the results of a randomized log space computation [email protected] without storing the output or randomness verbatim? Run- ning the algorithm again with new random bits may result Anurag Pandey in a new (and potentially different) output. We show that Max-Planck-Institut fuer Informatik every problem in search-RL has a randomized log-space [email protected] algorithm where the output can be reproduced. Specifi- cally, we show that for every problem in search-RL, there are a pair of log-space randomized algorithms A and B CP11 where for every input x, A will output some string tx of Pseudorandomness for Read-k DNF Formulas size O(log n), such that B when running on (x, tx) will be pseudo-deterministic: that is, running B multiple times The design of pseudorandom generators and determinis- on the same input (x, tx) will result in the same output tic approximate counting algorithms for DNF formulas on all executions with high probability. Thus, by storing are important challenges in unconditional derandomiza- only O(log n) bits in memory, it is possible to reproduce tion. Numerous works on these problems have focused on the output of a randomized log-space algorithm. An algo- the subclass of small-read DNF formulas, which are for- rithm is reproducible without storing any bits in memory mulas in which each variable occurs a bounded number of (i.e., |tx| = 0) if and only if it is pseudo-deterministic. We times. Our first main result is a pseudorandom genera- show pseudo-deterministic algorithms for finding paths in tor which ε-fools M-term read-k DNFs using seed length undirected graphs and Eulerian graphs using logarithmic poly(k, log(1/ε)) · log M + O(log n). This seed length is ex- space. Our algorithms are substantially faster than the ponentially shorter, as a function of both k and 1/ε,than best known deterministic algorithms for finding paths in the best previous PRG for read-k DNFs. We also give a DA19 Abstracts 15 deterministic algorithm that approximates the number of the first (1 + )-approximation algorithm running in time satisfying assignments of an M-term read-k DNF to any npoly(k/).Further,forp = 0, we give the first almost-linear desired (1 + ε)-multiplicative accuracy in time time approximation scheme for what we call the General- ˜ ized Binary 0-Rank-k problem. Our algorithm computes · poly(k,log(k/ε)) O(log((k log M)/ε)) O(k) 2 poly(n) min (M/ε) , (M/ε) . (1 + )-approximation in time (1/)2 / · nd1+o(1).On the hardness of approximation side, for p ∈ (1, 2), assum- The common essential ingredients in these pseudorandom- ing the Small Set Expansion Hypothesis and the Expo- ness results are new analytic inequalities for read-k DNFs. nential Time Hypothesis (ETH), we show that there exists These inequalities may be of independent interest and util- δ := δ(α) > 0 such that the entrywise p-Rank-k problem δ ity; as an example application, we use them to obtain a k significant improvement on the previous state of the art has no α-approximation algorithm running in time 2 . for agnostically learning read-k DNFs. Frank Ban Rocco A. Servedio, Li-Yang Tan UC Berkeley Columbia University [email protected] [email protected], [email protected] Vijay Bhattiprolu CMU CP11 [email protected] Near-optimal Bootstrapping of Hitting Sets for Al- gebraic Circuits Karl Bringmann Max Planck Institute for Informatics, The classical lemma of Ore-DeMillo-Lipton-Schwartz- Saarland Informatics Campus, Germany Zippel states that any nonzero n-variate degree-s polyno- [email protected] mial f, will evaluate to a nonzero value at some point on a grid Sn with |S| >s. Thus, there is a deterministic poly- nomial identity test (PIT) for all degree-s size-s algebraic Pavel Kolev circuits in n variables that runs in time poly(s)(s +1)n. MPI-INF In a surprising recent result, Agrawal, Ghosh and Saxena [email protected] (STOC’18) showed any deterministic blackbox PIT algo- rithm for degree-s,size-s, n-variate circuits with running Euiwoong Lee 0.5−δ time as bad as sn ,whereδ>0,canbeusedtocon- Carnegie Mellon University struct blackbox PIT algorithms for degree-s size s circuits [email protected] ∗ with running time sexp(exp(O(log s))). The authors asked if a similar conclusion followed if their hypothesis was weak- David Woodruff CMU ened to having deterministic PIT with running time so(n). [email protected] In this paper, we answer their question in the affirmative. We show that, given a deterministic blackbox PIT that o(n) runs in time (s )H(n), where H(n) is an arbitrary func- CP12 tion, for all degree-s size-s algebraic circuits over n vari- ables, we can obtain a deterministic blackbox PIT that Every Testable (Infinite) Property of Bounded- ∗ degree Graphs Contains An Infinite Hyperfinite runs in time sexp(exp(O(log s))) for all degree-s size-s alge- Subproperty braic circuits over n variables. In other words, any black- box PIT with just a slightly non-trivial exponent of s com- One of the most fundamental questions in graph prop- O(n) pared to the trivial s test can be used to give a nearly erty testing is to characterize the combinatorial structure polynomial time blackbox PIT algorithm. of properties that are testable with a constant number of queries. We work towards an answer to this question for Mrinal Kumar the bounded-degree graph model, where the input graphs Simons Institute for the Theory of Computing, Berkeley, have maximum degree bounded by a constant d.Inthis USA model, it is known (among other results) that every hy- [email protected] perfinite property is constant-query testable, where, infor- mally, a graph property is hyperfinite, if for every δ>0 Ramprasad Saptharishi every graph in the property can be partitioned into small Tata Institute of Fundamental Research, Mumbai, India connected components by removing δn edges. In this paper [email protected] we show that hyperfiniteness plays a role in every testable property, i.e. we show that every testable property is either Anamay Tengse finite (which trivially implies hyperfiniteness and testabil- Tata Institute of Fundamental Research, Mumbai ity) or contains an infinite hyperfinite subproperty. A sim- [email protected] ple consequence of our result is that no infinite graph prop- erty that only consists of expander graphs is constant-query testable. Based on the above findings, one could ask if ev- CP12 ery infinite testable non-hyperfinite property might contain A Ptas for p-Low Rank Approximation an infinite family of expander (or near-expander) graphs. We show that this is not true. A number of recent works have studied algorithms for entrywise p-low rank approximation, namely, algorithms Hendrik Fichtenberger which given an n×d matrix A (with n ≥ d), output a rank- TU Dortmund − p | − |p k matrix B minimizing A B p = i,j Ai,j Bi,j hendrik.fi[email protected] − when p>0; and A B 0 = i,j [Ai,j = Bi,j ]for p = 0. On the algorithmic side, for p ∈ (0, 2), we give Pan Peng 16 DA19 Abstracts University of Sheffield University of Wisconsin, Madison p.peng@sheffield.ac.uk [email protected] Christian Sohler Department of Computer Science CP12 Technische Universitat Dortmund Testing Matrix Rank, Optimally [email protected] We show that for the problem of testing if a matrix A ∈ F n×n has rank at most d, or requires changing an -fraction CP12 of entries to have rank at most d,thereisanon-adaptive Testing Halfspaces over Rotation-invariant Distri- query algorithm making O (d2/) queries. Our algorithm butions works for any field F . This improves upon the previ- ous O(d2/2) bound (SODA’03), and bypasses an Ω(d2/2) We present an algorithm for testing halfspaces over ar- lower bound of (KDD’14) which holds if the algorithm is re- bitrary,√ unknown rotation-invariant distributions. Using quired to read a submatrix. Our algorithm is the first such O ( n−7) random examples of an unknown function f,the algorithm which does not read a submatrix, and instead algorithm determines with high probability whether f is of reads a carefully selected non-adaptive pattern of entries − the form f(x)=sign( i wixi t)oris-far from all such in rows and columns of A. We complement our algorithm functions. This sample size is significantly smaller than with a matching query complexity lower bound for non- the well-known requirement of Ω(n) samples for learning adaptive testers over any field. We also give tight bounds halfspaces, and known lower bounds imply that our sample of Θ( d2) queries in the sensing model for which query access size is optimal up to logarithmic factors. The algorithm is comes in the form of Xi,A := tr(Xi A); perhaps surpris- distribution-free in the sense that it requires no knowledge ingly these bounds do not depend on . We next develop of the distribution aside from the promise of rotation in- a novel property testing framework for testing numerical variance. To prove the correctness of this algorithm we properties of a real-valued matrix A more generally, which present a theorem relating the distance between a function includes the stable rank, Schatten-p norms, and SVD en- and a halfspace to the distance between their centers of tropy. Specifically, we propose a bounded entry model, mass, that applies to arbitrary distributions. where A is required to have entries bounded by 1 in ab- solute value. We give upper and lower bounds for a wide Nathaniel Harms range of problems in this model, and discuss connections University of Waterloo to the sensing model above. [email protected] Maria-Florina Balcan Carnegie Mellon University CP12 [email protected] Anaconda: A Non-adaptive Conditional Sampling Algorithm for Distribution Testing Yi Li Nanyang Technological University We investigate distribution testing with access to non- [email protected] adaptive conditional samples. In the conditional sampling model, the algorithm is given the following access to a dis- David Woodruff, Hongyang Zhang tribution: it submits a query set S to an oracle, which Carnegie Mellon University returns a sample from the distribution conditioned on be- [email protected], [email protected] ing from S. In the non-adaptive setting, all query sets must be specified in advance of viewing the outcomes. Our main result is the first polylogarithmic-query algorithm for CP13 equivalence testing, deciding whether two unknown distri- butions are equal to or far from each other. This is an expo- Approximation of Trees by Self-nested Trees nential improvement over the previous best upper bound, and demonstrates that the complexity of the problem in The class of self-nested trees presents remarkable compres- this model is intermediate to the the complexity of the sion properties because of the systematic repetition of sub- problem in the standard sampling model and the adaptive trees in their structure. In this paper, we provide a bet- conditional sampling model. We also significantly improve ter combinatorial characterization of this specific family of the sample complexity for the easier problems of unifor- trees. In particular, we show from both theoretical and mity and identity testing. For the former, our algorithm practical viewpoints that complex queries can be quickly requires only O˜(log n) queries, matching the information- answered in self-nested trees compared to general trees. theoretic lower bound up to a O(log log n)-factor. Our al- We also present an approximation algorithm of a tree by a gorithm works by reducing the problem from 1-testing to self-nestedonethatcanbeusedinfastpredictionofedit ∞-testing, which enjoys a much cheaper sample complex- distance between two trees. ity. Necessitated by the limited power of the non-adaptive model, our algorithm is very simple to state. However, Romain Azais there are significant challenges in the analysis, due to the Inria complex structure of how two arbitrary distributions may [email protected] differ. Jean-Baptiste Durand Gautam Kamath Universit´e Grenoble Alpes MIT [email protected] [email protected] Christophe Godin Christos Tzamos Inria DA19 Abstracts 17 [email protected] Our new variant applies the median-of-medians algorithm for selecting pivots in order to circumvent the quadratic worst case. Indeed, we show that it uses at most n log n + CP13 1.6n comparisons for n large enough. We experimen- Simple and Fast BlockQuicksort using Lomuto’s tally confirm the theoretical estimates and show that the Partitioning Scheme new algorithm outperforms Heapsort by far and is only around 10% slower than Introsort (std::sort implementa- This paper presents simple variants of the BlockQuicksort tion of stdlibc++), which has a rather poor guarantee for algorithm described by Edelkamp and Weiss (ESA 2016). the worst case. We also simulate the worst case, which is The simplification is achieved by using Lomuto’s partition- only around 10% slower than the average case. In partic- ing scheme instead of Hoare’s crossing pointer technique ular, the new algorithm is a natural candidate to replace to partition the input. To achieve a robust sorting algo- Heapsort as a worst-case stopper in Introsort. rithm that works well on many different input types, the paper introduces a novel two-pivot variant of Lomuto’s par- Armin Weiß titioning scheme. A surprisingly simple twist to the generic Universit¨at Stuttgart two-pivot quicksort approach makes the algorithm robust. [email protected] The paper provides an analysis of the theoretical prop- erties of the proposed algorithms and compares them to Stefan Edelkamp their competitors. The analysis shows that Lomuto-based King’s College London approaches incur a higher average sorting cost than the [email protected] Hoare-based approach of BlockQuicksort. Moreover, the analysis is particularly useful to reason about pivot choices that suit the two-pivot approach. An extensive experi- CP14 mental study shows that, despite their worse theoretical Sublinear Algorithms for (Delta + 1) Vertex Col- behavior, the simpler variants perform as well as the orig- oring inal version of BlockQuicksort. Any graph with maximum degree Δ admits a proper vertex Martin Aum¨uller coloring with Δ + 1 colors that can be found via a simple IT University of Copenhagen sequential greedy algorithm in linear time and space. But [email protected] can one find such a coloring via a sublinear algorithm? We answer this fundamental question in the affirmative Nikolaj Hass for several canonical classes of sublinear algorithms includ- IT University Of Copenhagen ing graph streaming, sublinear time, and massively paral- [email protected] lel computation (MPC) algorithms. In particular, we de- sign (a) a single-pass semi-streaming algorithm in dynamic ˜ CP13 streams using O(n) space, (b) a sublinear-time algorithm in the standard query model√ that allows neighbor queries Lightweight Distributed Suffix Array Construction and pair queries using O˜(n n) time (which we also prove We present two new distributed suffix array construc- is optimal), and (c) a parallel algorithm in the massively ˜ tion algorithms. One of our algorithms requires parallel computation (MPC) model using O(n)memory only half as much memory as its competitor (PSAC) per machine and O(1) MPC rounds. At the core of our re- [Flick & Aluru, SC 2015], while achieving similar speed. In sults is a remarkably simple meta-algorithm for the (Δ+1) practice, we can compute on the same hardware suffix ar- coloring problem: Sample O(log n) colors for each vertex rays for text twice as large as PSAC. The other algorithm uniformly at random from the Δ + 1 colors; find a proper still requires less memory than PSAC but is faster on some coloring of the graph using the sampled colors. As our instances. As a by-product, we also engineered the first main result, we prove that the sampled set of colors with distributed string sorting algorithm. All of our algorithms high probability contains a proper coloring of the input are tested on text collections of up to 115 GB and running graph. The sublinear algorithms are then obtained by de- on 1280 cores. signing efficient algorithms for finding a proper coloring of the graph from the sampled colors in the corresponding Florian Kurpicz, Johannes Fischer models. TU Dortmund fl[email protected], johannes.fi[email protected] Sepehr Assadi, Yu Chen, Sanjeev Khanna dortmund.de University of Pennsylvania [email protected], [email protected], san- [email protected] CP13 Worst-case Efficient Sorting with QuickMergesort CP14 The two most prominent solutions for the sorting problem Optimal Distributed Coloring Algorithms for Pla- are Quicksort and Mergesort. While Quicksort is very fast nar Graphs in the Local Model on average, Mergesort additionally gives worst-case guar- antees, but needs extra space for a linear number of ele- In this paper, we consider distributed coloring for planar ments. Worst-case efficient in-place sorting, however, re- graphs with a small number of colors. Our main result is mains a challenge: the standard solution, Heapsort, suf- an optimal (up to a constant factor) O(log n) time algo- fers from a bad cache behavior and is also not overly fast rithm for 6-coloring planar graphs. Our algorithm is based for in-cache instances. In this work we present median- on a novel technique that in a nutshell detects small struc- of-medians QuickMergesort (MoMQuickMergesort), a new tures that can be easily colored given a proper coloring of variant of QuickMergesort, which combines Quicksort with the rest of the vertices and removes them from the graph Mergesort allowing the latter to be implemented in place. until the graph contains a small enough number of edges. 18 DA19 Abstracts We believe this technique might be of independent inter- vious state-of-the-art bounds for Triangle Detection and est. In addition, we present a lower bound for 4-coloring Enumeration were O˜(n2/3)andO˜(n3/4), respectively, due planar graphs that essentially shows that any algorithm to Izumi and Le Gall (PODC 2017). The main nov- (deterministic or randomized) for 4-coloring planar graphs elty in this work is a distributed graph partitioning algo- requires Ω(n) rounds. We therefore completely resolve the rithm. We show that in O˜(n1−δ ) rounds we can partition problems of 4-coloring and 6-coloring for planar graphs in theedgesetofthenetworkG =(V,E) into three parts the LOCAL model. E = Em ∪ Es ∪ Er such that Shiri Chechik • Each connected component induced by Em has mini- δ Tel-Aviv University, Israel mum degree Ω(n ) and conductance Ω(1/poly log(n)). [email protected] • The subgraph induced by Es has arboricity at most nδ. Doron Mukhtar •|E |≤|E|/6. Tel-Aviv University r [email protected] All our algorithms are based on the following generic frame- work, which we believe is of interest beyond this work. Roughly, we deal with Es by an algorithm that is efficient CP14 for low-arboricity graphs, and deal with Er using recursive Distributed Maximal Independent Set Using Small calls. For each connected component induced by Em,we Messagesu are able to simulate congested clique algorithms with small overhead by applying a routing algorithm due to Ghaffari, Abstract not available Kuhn, and Su (PODC 2017) for high conductance graphs. Mohsen Ghaffari ETH Zurich Yi-Jun Chang, Seth Pettie ghaff[email protected] University of Michigan [email protected], [email protected] CP14 Hengjie Zhang Oblivious Resampling Oracles and Parallel Algo- Tsinghua University rithms for the Lopsided Lov´asz Local Lemma [email protected] The Lov´asz Local Lemma (LLL) is a probabilistic tool which shows that, if a collection of “bad’ events B in a CP15 probability space are not too likely and not too interde- Improving the Smoothed Complexity of Flip for pendent, then there is a positive probability that no bad- Max-cut Problems events in B occur. Moser & Tardos (2010) gave sequential and parallel algorithms which transformed most applica- Finding locally optimal solutions for max-cut and max-k- tions of the variable-assignment LLL into efficient algo- cut are well-known PLS-complete problems. An instinctive rithms. A framework of Harvey & Vondr´ak (2015) based approach to finding such solutions is the FLIP method. on “resampling oracles’ extended this give very general se- Even though FLIP requires exponential time in worst- quential algorithms for other probability spaces satisfying case instances, it tends to terminate quickly in practical the Lopsided Lov´asz Local Lemma (LLLL). We describe a instances. To explain this discrepancy, the run-time of new structural property of resampling oracles which holds FLIP has been studied in the smoothed complexity frame- for all known resampling oracles, which we call “oblivious- work. Etscheid and R¨oglin (ACM Trans. Algorithms, ness.’ Essentially, it means that the interaction between 2017) showed that the smoothed complexity of FLIP for two bad-events B,B depends only on the randomness used max-cut is quasi-polynomial. Angel, Bubeck, Peres and to resample B. This has two major consequences. First, it Wei (STOC 2017) showed that the smoothed complexity of is the key to achieving a unified parallel LLLL algorithm, FLIP for max-cut in complete graphs is O(φ5n15.1), where giving the first RNC algorithms for rainbow perfect match- φ is an upper bound on the random edge-weight density ings and rainbow hamiltonian cycles of Kn. Second, this and n is the number of vertices in the input graph. In this property allows us to build LLLL probability spaces out work, we improve the run-time bound for complete graphs. of a relatively simple “atomic’ set of events. Using this We prove that the smoothed complexity of FLIP in com- framework, we get the first sequential resampling oracle plete graphs is O(φn7.83). Our results are based on a care- for rainbow perfect matchings on the complete s-uniform fully chosen matrix whose rank captures the run-time of (s) hypergraph Kn , and the first commutative resampling or- the method along with improved rank bounds for this ma- acle for hamiltonian cycles of Kn. trix and an improved union bound based on this matrix. In addition, our techniques provide a general framework David G. Harris for analyzing FLIP in the smoothed framework. We illus- University of Maryland trate this general framework by showing that the smoothed [email protected] complexity of FLIP for max-3-cut in complete graphs is polynomial and for max-k-cut in arbitrary graphs is quasi- polynomial. CP14 Distributed Triangle Detection via Expander De- Ali Bibak composition University of Illinois, Urbana-Champaign [email protected] We present improved distributed algorithms for triangle detection and its variants in the CONGEST model. We Charles A. Carlson show that Triangle Detection, Counting, and Enumeration University of Colorado Boulder canbesolvedinO˜(n1/2) rounds. In contrast, the pre- [email protected] DA19 Abstracts 19 Karthekeyan Chandrasekaran [email protected] Department of Industrial and Enterprise Systems Engineering University of Illinois, Urbana-Champaign CP15 [email protected] Computing all Wardrop Equilibria Parametrized by the Flow Demand CP15 We develop an algorithm that computes for a given undi- rected or directed network with flow-dependent piece-wise On the Number of Circuits in Regular Matroids linear edge cost functions all Wardrop equilibria as a func- (with Connections to Lattices and Codes) tion of the flow demand. Our algorithm is based on Katzenelson’s homotopy method for electrical networks. We show that for any regular matroid on m elements and The algorithm uses a bijection between vertex potentials any α ≥ 1, the number of α-minimum circuits, or circuits and flow excess vectors that is piecewise linear in the poten- whosesizeisatmostanα-multiple of the minimum size of tial space and where each linear segment can be interpreted 2 a circuit in the matroid is bounded by mO(α ). This gener- as an augmenting flow in a residual network. The algorithm alizes a result of Karger for the number of α-minimum cuts iteratively increases the excess of one or more vertex pairs in a graph. As a consequence, we obtain similar bounds until the bijection reaches a point of non-differentiability. on the number of α-shortest vectors in “totally unimod- Then, the next linear region is chosen in a simplex-like ular” lattices and on the number of α-minimum weight pivot step and the algorithm proceeds. We first show that codewords in “regular” codes. this algorithm correctly computes all Wardrop equilibria in undirected single-commodity networks along the chosen path of excess vectors. We then adapt our algorithm to also Rohit Gurjar work for discontinuous cost functions which allows to model Indian Institute of Technology Bombay directed edges and/or edge capacities. Our algorithm is [email protected] output-polynomial in non-degenerate instances where the solution curve never hits a point where the cost function of Nisheeth K. Vishnoi more than one edge becomes non-differentiable. For degen- EPFL erate instances we still obtain an output-polynomial algo- EPFL rithm computing the linear segments of the bijection by a [email protected] convex program. The latter technique also allows to handle multiple commodities. CP15 Philipp Warode, Max Klimm School of Business and Economics, HU Berlin Nash Flows over Time with Spillback [email protected], [email protected] Modeling traffic in road networks is a widely studied but challenging problem, especially under the assumption that CP15 drivers act selfishly. A common approach used in simula- Minimum Cut and Minimum k-Cut in Hyper- tion software is the deterministic queuing model, for which graphs via Branching Contractions the structure of dynamic equilibria has been studied exten- sively in the last couple of years. The basic idea is to model Random edge contractions (Karger, SODA ’93) provide a traffic by a continuous flow that travels over time from a particularly simple and elegant method for computing the source to a sink through a network, in which the arcs are minimum cut of a graph. Recent work (Chandrasekharan, endowed with transit times and capacities. Whenever the Xu, and Yu, SODA ’18; Ghaffari, Karger, and Panigrahi, flow rate exceeds the capacity a queue builds up and the SODA ’17) has extended the random contraction technique infinitesimally small flow particles wait in line in front of from graphs to finding minimum cuts and minimum k-cuts the bottleneck. It was not possible, until now, to repre- in hypergraphs. We further this line of work by providing sent spillback in this model, which was a big drawback, a branching randomized contraction technique for hyper- since spillback has a huge impact on travel times in highly graphs. Our algorithms perform branches as a random congested regions. We extend the model by introducing response to the size of a hyperedge selected for contrac- a storage capacity that bounds the total amount of flow tion. The analysis then hinges on being able to quantify on each arc. If an arc gets full, the inflow capacity is re- the (random) structure of the resulting computation tree duced to the current outflow rate, which can cause queues and the randomness of the contracted hypergraph. This on previous arcs, i.e., spillback. We carry over the main new technique leads to the following improvements in run- results of the original model to our generalization and char- ning time (m is the number of hyperedges, n the number acterize dynamic equilibria, called Nash flows over time, by of vertices, and p the total size of all hyperedges): sequences of particular static flows, we call spillback thin • We give an algorithm that runs in O mn2k−2 time flows. Furthermore, we give a constructive proof for the ex- for finding a minimum k-cut in hypergraphs of ar- istence of dynamic equilibria, which suggests an algorithm bitrary rank (maximum size of a hyperedge). This for their computation. This solves an open problem stated improves the best known running time for k>2. by Koch and Skutella in 2010. • We give another algorithm that runs in max{r,2k−2} Leon Sering O n time for finding a minimum Institute of Mathematics - Technische Universit¨at Berlin k-cut in hypergraphs of constant rank r. This betters [email protected] the best known running times for dense hypergraphs. Our techniques and results extend to the problems of min- Laura Vargas Koch imum hedge-cut and minimum hedge-k-cut as well. School of Business and Economics - RWTH Aachen University Kyle Fox 20 DA19 Abstracts Department of Computer Science rapid mixing for degree sequences satisfying strong stabil- The University of Texas at Dallas ity, a notion closely related to P -stability. This results in a [email protected] much shorter proof that unifies and extends the currently known rapid mixing results for the switch chain. In partic- Debmalya Panigrahi ular, our work resolves an open problem posed by Greenhill Duke University (SODA, 2015). Secondly, in order to illustrate the power of [email protected] our approach, we study the problem of uniformly sampling graphs for which—in addition to the degree sequence—a Fred Zhang joint degree distribution is given. Although the problem Harvard University was formalized over a decade ago, small progress has been [email protected] made on the random sampling of such graphs. The case of a single degree class reduces to sampling of regular graphs, but beyond this almost nothing is known. We fully resolve CP16 the case of two degree classes, by showing that the switch Markov chain is always rapidly mixing. On the Rank of a Random Sparse Binary Matrix × Georgios Amanatidis, Pieter Kleer We study the rank of a random n m matrix An,m;k with Centrum Wiskunde & Informatica (CWI) entries from GF (2), and exactly k unit entries in each col- [email protected], [email protected] umn, the other entries being zero. The columns are chosen independently and uniformly at random from the set of n all k such columns. We obtain an asymptotically cor- CP16 rect estimate for the rank as a function of the number of On Coalescence Time in Graphs-When Is Coalesc- columns m in terms of c, n, k,andwherem = cn/k.The ingasFastasMeeting? matrix An,m;k forms the vertex-edge incidence matrix of a k-uniform random hypergraph H. The rank of An,m;k can Coalescing random walks is a fundamental stochastic pro- | | be expressed as follows. Let C2 be the number of vertices cess, where a set of particles perform independent discrete- of the 2-core of H,and|E(C2)| the number of edges. Let ∗ time random walks on an undirected graph. Whenever two m be the value of m for which |C2| = |E(C2)|. Then w.h.p. ∗ or more particles meet at a given node, they merge and con- for m