Lecture Notes in Computer Science 1853 Edited by G
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Benchmarks for IP Forwarding Tables
Reviewers James Abello Richard Cleve Vassos Hadzilacos Dimitris Achilioptas James Clippinger Jim Hafner Micah Adler Anne Condon Torben Hagerup Oswin Aichholzer Stephen Cook Armin Haken William Aiello Tom Cormen Shai Halevi Donald Aingworth Dan Dooly Eric Hansen Susanne Albers Oliver Duschka Refael Hassin Eric Allender Martin Dyer Johan Hastad Rajeev Alur Ran El-Yaniv Lisa Hellerstein Andris Ambainis David Eppstein Monika Henzinger Amihood Amir Jeff Erickson Tom Henzinger Artur Andrzejak Kousha Etessami Jeremy Horwitz Boris Aronov Will Evans Russell Impagliazzo Sanjeev Arora Guy Even Piotr Indyk Amotz Barnoy Ron Fagin Sandra Irani Yair Bartal Michalis Faloutsos Ken Jackson Julien Basch Martin Farach-Colton David Johnson Saugata Basu Uri Feige John Jozwiak Bob Beals Joan Feigenbaum Bala Kalyandasundaram Paul Beame Stefan Felsner Ming-Yang Kao Steve Bellantoni Faith Fich Haim Kaplan Micahel Ben-Or Andy Fingerhut Bruce Kapron Josh Benaloh Paul Fischer Michael Kaufmann Charles Bennett Lance Fortnow Michael Kearns Marshall Bern Steve Fortune Sanjeev Khanna Nikolaj Bjorner Alan Frieze Samir Khuller Johannes Blomer Anna Gal Joe Kilian Avrim Blum Naveen Garg Valerie King Dan Boneh Bernd Gartner Philip Klein Andrei Broder Rosario Gennaro Spyros Kontogiannis Nader Bshouty Ashish Goel Gilad Koren Adam Buchsbaum Michel Goemans Dexter Kozen Lynn Burroughs Leslie Goldberg Dina Kravets Ran Canetti Paul Goldberg S. Ravi Kumar Pei Cao Oded Goldreich Eyal Kushilevitz Moses Charikar John Gray Stephen Kwek Chandra Chekuri Dan Greene Larry Larmore Yi-Jen Chiang -
Resource-Competitive Algorithms1 1 Introduction
Resource-Competitive Algorithms1 Michael A. Bender Varsha Dani Department of Computer Science Department of Computer Science Stony Brook University University of New Mexico Stony Brook, NY, USA Albuquerque, NM, USA [email protected] [email protected] Jeremy T. Fineman Seth Gilbert Department of Computer Science Department of Computer Science Georgetown University National University of Singapore Washington, DC, USA Singapore [email protected] [email protected] Mahnush Movahedi Seth Pettie Department of Computer Science Electrical Eng. and Computer Science Dept. University of New Mexico University of Michigan Albuquerque, NM, USA Ann Arbor, MI, USA [email protected] [email protected] Jared Saia Maxwell Young Department of Computer Science Computer Science and Engineering Dept. University of New Mexico Mississippi State University Albuquerque, NM, USA Starkville, MS, USA [email protected] [email protected] Abstract The point of adversarial analysis is to model the worst-case performance of an algorithm. Un- fortunately, this analysis may not always reflect performance in practice because the adversarial assumption can be overly pessimistic. In such cases, several techniques have been developed to provide a more refined understanding of how an algorithm performs e.g., competitive analysis, parameterized analysis, and the theory of approximation algorithms. Here, we describe an analogous technique called resource competitiveness, tailored for dis- tributed systems. Often there is an operational cost for adversarial behavior arising from band- width usage, computational power, energy limitations, etc. Modeling this cost provides some notion of how much disruption the adversary can inflict on the system. In parameterizing by this cost, we can design an algorithm with the following guarantee: if the adversary pays T , then the additional cost of the algorithm is some function of T . -
FOCS 2005 Program SUNDAY October 23, 2005
FOCS 2005 Program SUNDAY October 23, 2005 Talks in Grand Ballroom, 17th floor Session 1: 8:50am – 10:10am Chair: Eva´ Tardos 8:50 Agnostically Learning Halfspaces Adam Kalai, Adam Klivans, Yishay Mansour and Rocco Servedio 9:10 Noise stability of functions with low influences: invari- ance and optimality The 46th Annual IEEE Symposium on Elchanan Mossel, Ryan O’Donnell and Krzysztof Foundations of Computer Science Oleszkiewicz October 22-25, 2005 Omni William Penn Hotel, 9:30 Every decision tree has an influential variable Pittsburgh, PA Ryan O’Donnell, Michael Saks, Oded Schramm and Rocco Servedio Sponsored by the IEEE Computer Society Technical Committee on Mathematical Foundations of Computing 9:50 Lower Bounds for the Noisy Broadcast Problem In cooperation with ACM SIGACT Navin Goyal, Guy Kindler and Michael Saks Break 10:10am – 10:30am FOCS ’05 gratefully acknowledges financial support from Microsoft Research, Yahoo! Research, and the CMU Aladdin center Session 2: 10:30am – 12:10pm Chair: Satish Rao SATURDAY October 22, 2005 10:30 The Unique Games Conjecture, Integrality Gap for Cut Problems and Embeddability of Negative Type Metrics Tutorials held at CMU University Center into `1 [Best paper award] Reception at Omni William Penn Hotel, Monongahela Room, Subhash Khot and Nisheeth Vishnoi 17th floor 10:50 The Closest Substring problem with small distances Tutorial 1: 1:30pm – 3:30pm Daniel Marx (McConomy Auditorium) Chair: Irit Dinur 11:10 Fitting tree metrics: Hierarchical clustering and Phy- logeny Subhash Khot Nir Ailon and Moses Charikar On the Unique Games Conjecture 11:30 Metric Embeddings with Relaxed Guarantees Break 3:30pm – 4:00pm Ittai Abraham, Yair Bartal, T-H. -
A Polynomial-Time Approximation Algorithm for All-Terminal Network Reliability
A Polynomial-Time Approximation Algorithm for All-Terminal Network Reliability Heng Guo School of Informatics, University of Edinburgh, Informatics Forum, Edinburgh, EH8 9AB, United Kingdom. [email protected] https://orcid.org/0000-0001-8199-5596 Mark Jerrum1 School of Mathematical Sciences, Queen Mary, University of London, Mile End Road, London, E1 4NS, United Kingdom. [email protected] https://orcid.org/0000-0003-0863-7279 Abstract We give a fully polynomial-time randomized approximation scheme (FPRAS) for the all-terminal network reliability problem, which is to determine the probability that, in a undirected graph, assuming each edge fails independently, the remaining graph is still connected. Our main contri- bution is to confirm a conjecture by Gorodezky and Pak (Random Struct. Algorithms, 2014), that the expected running time of the “cluster-popping” algorithm in bi-directed graphs is bounded by a polynomial in the size of the input. 2012 ACM Subject Classification Theory of computation → Generating random combinatorial structures Keywords and phrases Approximate counting, Network Reliability, Sampling, Markov chains Digital Object Identifier 10.4230/LIPIcs.ICALP.2018.68 Related Version Also available at https://arxiv.org/abs/1709.08561. Acknowledgements We thank Mark Huber for bringing reference [8] to our attention, Mark Walters for the coupling idea leading to Lemma 12, and Igor Pak for comments on an earlier version. We also thank the organizers of the “LMS – EPSRC Durham Symposium on Markov Processes, Mixing Times and Cutoff”, where part of the work is carried out. 1 Introduction Network reliability problems are extensively studied #P-hard problems [5] (see also [3, 22, 18, 2]). -
Arxiv:1611.01647V4 [Cs.DS] 15 Jan 2019 Resample to Able of Are Class We Special Techniques, Our a with for Inst Extremal Solutions Answer
UNIFORM SAMPLING THROUGH THE LOVASZ´ LOCAL LEMMA HENG GUO, MARK JERRUM, AND JINGCHENG LIU Abstract. We propose a new algorithmic framework, called “partial rejection sampling”, to draw samples exactly from a product distribution, conditioned on none of a number of bad events occurring. Our framework builds new connections between the variable framework of the Lov´asz Local Lemma and some classical sampling algorithms such as the “cycle-popping” algorithm for rooted spanning trees. Among other applications, we discover new algorithms to sample satisfying assignments of k-CNF formulas with bounded variable occurrences. 1. Introduction The Lov´asz Local Lemma [9] is a classical gem in combinatorics that guarantees the existence of a perfect object that avoids all events deemed to be “bad”. The original proof is non- constructive but there has been great progress in the algorithmic aspects of the local lemma. After a long line of research [3, 2, 30, 8, 34, 37], the celebrated result by Moser and Tardos [31] gives efficient algorithms to find such a perfect object under conditions that match the Lov´asz Local Lemma in the so-called variable framework. However, it is natural to ask whether, under the same condition, we can also sample a perfect object uniformly at random instead of merely finding one. Roughly speaking, the resampling algorithm by Moser and Tardos [31] works as follows. We initialize all variables randomly. If bad events occur, then we arbitrarily choose a bad event and resample all the involved variables. Unfortunately, it is not hard to see that this algorithm can produce biased samples. -
Arxiv:2008.08417V2 [Cs.DS] 3 Nov 2020
Modular Subset Sum, Dynamic Strings, and Zero-Sum Sets Jean Cardinal∗ John Iacono† Abstract a number of operations on a persistent collection strings, e modular subset sum problem consists of deciding, given notably, split and concatenate, as well as nding the longest a modulus m, a multiset S of n integers in 0::m − 1, and common prex (LCP) of two strings, all in at most loga- a target integer t, whether there exists a subset of S with rithmic time with updates being with high probability. We elements summing to t (mod m), and to report such a inform the reader of the independent work of Axiotis et + set if it exists. We give a simple O(m log m)-time with al., [ABB 20], contains much of the same results and ideas high probability (w.h.p.) algorithm for the modular sub- for modular subset sum as here has been accepted to ap- set sum problem. is builds on and improves on a pre- pear along with this paper in the proceedings of the SIAM vious O(m log7 m) w.h.p. algorithm from Axiotis, Backurs, Symposium on Simplicity in Algorithms (SOSA21). + Jin, Tzamos, and Wu (SODA 19). Our method utilizes the As the dynamic string data structure of [GKK 18] is ADT of the dynamic strings structure of Gawrychowski quite complex, we provide in §4 a new and far simpler alter- et al. (SODA 18). However, as this structure is rather com- native, which we call the Data Dependent Tree (DDT) struc- plicated we present a much simpler alternative which we ture. -
The Best Nurturers in Computer Science Research
The Best Nurturers in Computer Science Research Bharath Kumar M. Y. N. Srikant IISc-CSA-TR-2004-10 http://archive.csa.iisc.ernet.in/TR/2004/10/ Computer Science and Automation Indian Institute of Science, India October 2004 The Best Nurturers in Computer Science Research Bharath Kumar M.∗ Y. N. Srikant† Abstract The paper presents a heuristic for mining nurturers in temporally organized collaboration networks: people who facilitate the growth and success of the young ones. Specifically, this heuristic is applied to the computer science bibliographic data to find the best nurturers in computer science research. The measure of success is parameterized, and the paper demonstrates experiments and results with publication count and citations as success metrics. Rather than just the nurturer’s success, the heuristic captures the influence he has had in the indepen- dent success of the relatively young in the network. These results can hence be a useful resource to graduate students and post-doctoral can- didates. The heuristic is extended to accurately yield ranked nurturers inside a particular time period. Interestingly, there is a recognizable deviation between the rankings of the most successful researchers and the best nurturers, which although is obvious from a social perspective has not been statistically demonstrated. Keywords: Social Network Analysis, Bibliometrics, Temporal Data Mining. 1 Introduction Consider a student Arjun, who has finished his under-graduate degree in Computer Science, and is seeking a PhD degree followed by a successful career in Computer Science research. How does he choose his research advisor? He has the following options with him: 1. Look up the rankings of various universities [1], and apply to any “rea- sonably good” professor in any of the top universities. -
On the Complexity of Partial Derivatives Ignacio Garcia-Marco, Pascal Koiran, Timothée Pecatte, Stéphan Thomassé
On the complexity of partial derivatives Ignacio Garcia-Marco, Pascal Koiran, Timothée Pecatte, Stéphan Thomassé To cite this version: Ignacio Garcia-Marco, Pascal Koiran, Timothée Pecatte, Stéphan Thomassé. On the complexity of partial derivatives. 2016. ensl-01345746v2 HAL Id: ensl-01345746 https://hal-ens-lyon.archives-ouvertes.fr/ensl-01345746v2 Preprint submitted on 30 May 2017 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. On the complexity of partial derivatives Ignacio Garcia-Marco, Pascal Koiran, Timothée Pecatte, Stéphan Thomassé LIP,∗ Ecole Normale Supérieure de Lyon, Université de Lyon. May 30, 2017 Abstract The method of partial derivatives is one of the most successful lower bound methods for arithmetic circuits. It uses as a complexity measure the dimension of the span of the partial derivatives of a polynomial. In this paper, we consider this complexity measure as a computational problem: for an input polynomial given as the sum of its nonzero monomials, what is the complexity of computing the dimension of its space of partial derivatives? We show that this problem is ♯P-hard and we ask whether it belongs to ♯P. We analyze the “trace method”, recently used in combinatorics and in algebraic complexity to lower bound the rank of certain matri- ces. -
Interactions of Computational Complexity Theory and Mathematics
Interactions of Computational Complexity Theory and Mathematics Avi Wigderson October 22, 2017 Abstract [This paper is a (self contained) chapter in a new book on computational complexity theory, called Mathematics and Computation, whose draft is available at https://www.math.ias.edu/avi/book]. We survey some concrete interaction areas between computational complexity theory and different fields of mathematics. We hope to demonstrate here that hardly any area of modern mathematics is untouched by the computational connection (which in some cases is completely natural and in others may seem quite surprising). In my view, the breadth, depth, beauty and novelty of these connections is inspiring, and speaks to a great potential of future interactions (which indeed, are quickly expanding). We aim for variety. We give short, simple descriptions (without proofs or much technical detail) of ideas, motivations, results and connections; this will hopefully entice the reader to dig deeper. Each vignette focuses only on a single topic within a large mathematical filed. We cover the following: • Number Theory: Primality testing • Combinatorial Geometry: Point-line incidences • Operator Theory: The Kadison-Singer problem • Metric Geometry: Distortion of embeddings • Group Theory: Generation and random generation • Statistical Physics: Monte-Carlo Markov chains • Analysis and Probability: Noise stability • Lattice Theory: Short vectors • Invariant Theory: Actions on matrix tuples 1 1 introduction The Theory of Computation (ToC) lays out the mathematical foundations of computer science. I am often asked if ToC is a branch of Mathematics, or of Computer Science. The answer is easy: it is clearly both (and in fact, much more). Ever since Turing's 1936 definition of the Turing machine, we have had a formal mathematical model of computation that enables the rigorous mathematical study of computational tasks, algorithms to solve them, and the resources these require. -
A Complete Bibliography of Publications in the Journal of Computer and System Sciences
A Complete Bibliography of Publications in the Journal of Computer and System Sciences Nelson H. F. Beebe University of Utah Department of Mathematics, 110 LCB 155 S 1400 E RM 233 Salt Lake City, UT 84112-0090 USA Tel: +1 801 581 5254 FAX: +1 801 581 4148 E-mail: [email protected], [email protected], [email protected] (Internet) WWW URL: http://www.math.utah.edu/~beebe/ 26 May 2021 Version 1.04 Title word cross-reference (s; t) [1475]. 0(n) [1160]. 1 [270]. 1 − L [1371, 924]. 12n2 [450]. 2 [3525, 3184, 2191, 1048, 3402, 1500, 3364, 2034, 2993, 834, 2473, 3101]. f2; 3g [2843]. #09111 [1814]. #AM01053M 2pn 1=3 − 2 O(n ) [1862, 1877, 1904]. #better 5X8 els [1856]. 2 [2353]. 2 [445]. 2 [1977]. 3 #better 5X8 els.pdf [1856]. #BIS [3184]. [1920, 2242, 2827, 2374, 1961, 2825, 3240, · #BIS-hardness #CSP 1071, 1133]. 43 [3620]. 4 Log2N [655]. 5=4 [3184]. ∗ 0 #econdirectM [1612]. 8 [2998]. =? [1433]. [858]. [2696, 2908, 2500, 3508]. 1 ∗ NP [1893]. #HA03022M [1860, 1875]. [1625, 3418, 1085]. [1523]. [3194]. NP[O(log n)] p ⊆ PH #HA05062N [1876]. #HA05062O [2235]. [995]. 2 [2235]. [1849, 1861]. #HA06043M [1501]. 2 [3418]. H [1565]. a [426, 289]. [1892, 1871, 1889]. #HA08111M [1846]. A[P (x); 2x; x + 1] [500]. Ax = λBx [377]. b · #P [1195, 3598, 1261, 1264]. [3253]. β [3444]. [3418]. d #praise 5X8 els.pdf [1832]. [2527, 1362, 3256, 3563]. δ [3553]. `p [2154]. #sciencejobs N [1802]. [1551, 2617, 1864, 1693]. g [3312]. h [1019]. K [1320, 2756, 191, 3494, 1300, 1546, 3286, #P [1373]. -
Kurt Mehlhorn, and Monique Teillaud
Volume 1, Issue 3, March 2011 Reasoning about Interaction: From Game Theory to Logic and Back (Dagstuhl Seminar 11101) Jürgen Dix, Wojtek Jamroga, and Dov Samet .................................... 1 Computational Geometry (Dagstuhl Seminar 11111) Pankaj Kumar Agarwal, Kurt Mehlhorn, and Monique Teillaud . 19 Computational Complexity of Discrete Problems (Dagstuhl Seminar 11121) Martin Grohe, Michal Koucký, Rüdiger Reischuk, and Dieter van Melkebeek . 42 Exploration and Curiosity in Robot Learning and Inference (Dagstuhl Seminar 11131) Jeremy L. Wyatt, Peter Dayan, Ales Leonardis, and Jan Peters . 67 DagstuhlReports,Vol.1,Issue3 ISSN2192-5283 ISSN 2192-5283 Published online and open access by Aims and Scope Schloss Dagstuhl – Leibniz-Zentrum für Informatik The periodical Dagstuhl Reports documents the GmbH, Dagstuhl Publishing, Saarbrücken/Wadern, program and the results of Dagstuhl Seminars and Germany. Dagstuhl Perspectives Workshops. Online available at http://www.dagstuhl.de/dagrep In principal, for each Dagstuhl Seminar or Dagstuhl Perspectives Workshop a report is published that Publication date contains the following: August, 2011 an executive summary of the seminar program and the fundamental results, Bibliographic information published by the Deutsche an overview of the talks given during the seminar Nationalbibliothek (summarized as talk abstracts), and The Deutsche Nationalbibliothek lists this publica- summaries from working groups (if applicable). tion in the Deutsche Nationalbibliografie; detailed This basic framework can be extended by suitable bibliographic data are available in the Internet at contributions that are related to the program of the http://dnb.d-nb.de. seminar, e.g. summaries from panel discussions or open problem sessions. License This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported license: CC-BY-NC-ND. -
The Complexity of Nash Equilibria by Constantinos Daskalakis Diploma
The Complexity of Nash Equilibria by Constantinos Daskalakis Diploma (National Technical University of Athens) 2004 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Christos H. Papadimitriou, Chair Professor Alistair J. Sinclair Professor Satish Rao Professor Ilan Adler Fall 2008 The dissertation of Constantinos Daskalakis is approved: Chair Date Date Date Date University of California, Berkeley Fall 2008 The Complexity of Nash Equilibria Copyright 2008 by Constantinos Daskalakis Abstract The Complexity of Nash Equilibria by Constantinos Daskalakis Doctor of Philosophy in Computer Science University of California, Berkeley Professor Christos H. Papadimitriou, Chair The Internet owes much of its complexity to the large number of entities that run it and use it. These entities have different and potentially conflicting interests, so their interactions are strategic in nature. Therefore, to understand these interactions, concepts from Economics and, most importantly, Game Theory are necessary. An important such concept is the notion of Nash equilibrium, which provides us with a rigorous way of predicting the behavior of strategic agents in situations of conflict. But the credibility of the Nash equilibrium as a framework for behavior-prediction depends on whether such equilibria are efficiently computable. After all, why should we expect a group of rational agents to behave in a fashion that requires exponential time to be computed? Motivated by this question, we study the computational complexity of the Nash equilibrium. We show that computing a Nash equilibrium is an intractable problem.