Programming (ISMP 2015), the Most Important Meeting of the Mathematical Optimization Society (MOS)
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Arkadi Nemirovski (PDF)
Arkadi Nemirovski Mr. Chancellor, I present Arkadi Nemirovski. For more than thirty years, Arkadi Nemirovski has been a leader in continuous optimization. He has played central roles in three of the major breakthroughs in optimization, namely, the ellipsoid method for convex optimization, the extension of modern interior-point methods to convex optimization, and the development of robust optimization. All of these contributions have had tremendous impact, going significantly beyond the original domain of the discoveries in their theoretical and practical consequences. In addition to his outstanding work in optimization, Professor Nemirovski has also made major contributions in the area of nonparametric statistics. Even though the most impressive part of his work is in theory, every method, every algorithm he proposes comes with a well-implemented computer code. Professor Nemirovski is widely admired not only for his brilliant scientific contributions, but also for his gracious humility, kindness, generosity, and his dedication to students and colleagues. Professor Nemirovski has held chaired professorships and many visiting positions at top research centres around the world, including an adjunct professor position in the Department of Combinatorics and Optimization at Waterloo from 2000-2004. Since 2005, Professor Nemirovski has been John Hunter Professor in the School of Industrial and Systems Engineering at the Georgia Institute of Technology. Among many previous honours, he has received the Fulkerson Prize from the American Mathematical Society and the Mathematical Programming Society in 1982, the Dantzig Prize from the Society for Industrial and Applied Mathematics and the Mathematical Programming Society in 1991, and the John von Neumann Theory Prize from the Institute for Operations Research and the Management Sciences in 2003. -
Some Aspects of the Optimization Over the Copositive and the Completely Positive Cone
Some aspects of the optimization over the copositive and the completely positive cone Dissertation zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften (Dr. rer. nat.) Dem Fachbereich IV der Universit¨at Trier vorgelegt von Julia Witzel Trier, 2013 Eingereicht am 17.07.2013 Gutachter: Prof. Dr. Mirjam Dur¨ Prof. Dr. Gabriele Eichfelder Tag der mundlichen¨ Prufung:¨ 25.09.2013 Die Autorin hat im Laufe des Promotionsverfahrens geheiratet und hieß zu dessen Beginn Julia Sponsel. Zusammenfassung Optimierungsprobleme tauchen in vielen praktischen Anwendungsbereichen auf. Zu den einfachsten Problemen geh¨oren lineare Optimierungsprobleme. Oft reichen lineare Probleme allerdings zur Modellierung praktischer Anwendun- gen nicht aus, sondern es muss die Ganzzahligkeit von Variablen gefordert werden oder es tauchen nichtlineare Nebenbedingungen oder eine nichtlineare Zielfunktion auf. Eine wichtige Problemklasse stellen hierbei quadratische Pro- bleme dar, welche im Allgemeinen aber schwer zu l¨osen sind. Ein Ansatz, der sich in den letzten Jahren entwickelt und verbreitet hat, besteht in der Umfor- mulierung quadratischer Probleme in sogenannte kopositive Programme. Ko- positive Programme sind lineare Optimierungsprobleme uber¨ dem Kegel der kopositiven Matrizen n×n T T n Cn = fA 2 R : A = A ; x Ax ≥ 0 fur¨ alle x 2 R ; x ≥ 0g; oder dem Dualkegel, dem Kegel der vollst¨andig positiven Matrizen m ∗ n X T n o Cn = xixi : xi 2 R ; xi ≥ 0 fur¨ alle i = 1; : : : ; m : i=1 Die Schwierigkeit dieser Probleme liegt in der Kegelbedingung. Aber nicht nur das Optimieren uber¨ diesen Kegeln ist schwierig. Wie von Murty und Kabadi (1987) und Dickinson und Gijben (2013) gezeigt ist es NP-schwer zu testen, ob eine gegebene Matrix kopositiv oder vollst¨andig positiv ist. -
Using the COIN-OR Server
Using the COIN-OR Server Your CoinEasy Team November 16, 2009 1 1 Overview This document is part of the CoinEasy project. See projects.coin-or.org/CoinEasy. In this document we describe the options available to users of COIN-OR who are interested in solving opti- mization problems but do not wish to compile source code in order to build the COIN-OR projects. In particular, we show how the user can send optimization problems to a COIN-OR server and get the solution result back. The COIN-OR server, webdss.ise.ufl.edu, is 2x Intel(R) Xeon(TM) CPU 3.06GHz 512MiB L2 1024MiB L3, 2GiB DRAM, 4x73GiB scsi disk 2xGigE machine. This server allows the user to directly access the following COIN-OR optimization solvers: • Bonmin { a solver for mixed-integer nonlinear optimization • Cbc { a solver for mixed-integer linear programs • Clp { a linear programming solver • Couenne { a solver for mixed-integer nonlinear optimization problems and is capable of global optiomization • DyLP { a linear programming solver • Ipopt { an interior point nonlinear optimization solver • SYMPHONY { mixed integer linear solver that can be executed in either parallel (dis- tributed or shared memory) or sequential modes • Vol { a linear programming solver All of these solvers on the COIN-OR server may be accessed through either the GAMS or AMPL modeling languages. In Section 2.1 we describe how to use the solvers using the GAMS modeling language. In Section 2.2 we describe how to call the solvers using the AMPL modeling language. In Section 3 we describe how to call the solvers using a command line executable pro- gram OSSolverService.exe (or OSSolverService for Linux/Mac OS X users { in the rest of the document we refer to this executable using a .exe extension). -
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model
Generating Single and Multiple Cooperative Heuristics for the One Dimensional Bin Packing Problem Using a Single Node Genetic Programming Island Model Kevin Sim Emma Hart Institute for Informatics and Digital Innovation Institute for Informatics and Digital Innovation Edinburgh Napier University Edinburgh Napier University Edinburgh, Scotland, UK Edinburgh, Scotland, UK [email protected] [email protected] ABSTRACT exhibit superior performance than similar human designed Novel deterministic heuristics are generated using Single Node heuristics and can be used to produce collections of heuris- Genetic Programming for application to the One Dimen- tics that collectively maximise the potential of selective HH. sional Bin Packing Problem. First a single deterministic The papers contribution is two fold. Single heuristics ca- heuristic was evolved that minimised the total number of pable of outperforming a range of well researched deter- bins used when applied to a set of 685 training instances. ministic heuristics are generated by combining features ex- Following this, a set of heuristics were evolved using a form tracted from those heuristics. Secondly an island model[19] of cooperative co-evolution that collectively minimise the is adapted to use multiple SNGP implementations to gener- number of bins used across the same set of problems. Re- ate diverse sets of heuristics which collectively outperform sults on an unseen test set comprising a further 685 prob- any of the single heuristics when used in isolation. Both ap- lem instances show that the single evolved heuristic out- proaches are trained and evaluated using equal divisions of a performs existing deterministic heuristics described in the large set of 1370 benchmark problem instances sourced from literature. -
13. Mathematics University of Central Oklahoma
Southwestern Oklahoma State University SWOSU Digital Commons Oklahoma Research Day Abstracts 2013 Oklahoma Research Day Jan 10th, 12:00 AM 13. Mathematics University of Central Oklahoma Follow this and additional works at: https://dc.swosu.edu/ordabstracts Part of the Animal Sciences Commons, Biology Commons, Chemistry Commons, Computer Sciences Commons, Environmental Sciences Commons, Mathematics Commons, and the Physics Commons University of Central Oklahoma, "13. Mathematics" (2013). Oklahoma Research Day Abstracts. 12. https://dc.swosu.edu/ordabstracts/2013oklahomaresearchday/mathematicsandscience/12 This Event is brought to you for free and open access by the Oklahoma Research Day at SWOSU Digital Commons. It has been accepted for inclusion in Oklahoma Research Day Abstracts by an authorized administrator of SWOSU Digital Commons. An ADA compliant document is available upon request. For more information, please contact [email protected]. Abstracts from the 2013 Oklahoma Research Day Held at the University of Central Oklahoma 05. Mathematics and Science 13. Mathematics 05.13.01 A simplified proof of the Kantorovich theorem for solving equations using scalar telescopic series Ioannis Argyros, Cameron University The Kantorovich theorem is an important tool in Mathematical Analysis for solving nonlinear equations in abstract spaces by approximating a locally unique solution using the popular Newton-Kantorovich method.Many proofs have been given for this theorem using techniques such as the contraction mapping principle,majorizing sequences, recurrent functions and other techniques.These methods are rather long,complicated and not very easy to understand in general by undergraduate students.In the present paper we present a proof using simple telescopic series studied first in a Calculus II class. -
2019 AMS Prize Announcements
FROM THE AMS SECRETARY 2019 Leroy P. Steele Prizes The 2019 Leroy P. Steele Prizes were presented at the 125th Annual Meeting of the AMS in Baltimore, Maryland, in January 2019. The Steele Prizes were awarded to HARUZO HIDA for Seminal Contribution to Research, to PHILIppE FLAJOLET and ROBERT SEDGEWICK for Mathematical Exposition, and to JEFF CHEEGER for Lifetime Achievement. Haruzo Hida Philippe Flajolet Robert Sedgewick Jeff Cheeger Citation for Seminal Contribution to Research: Hamadera (presently, Sakai West-ward), Japan, he received Haruzo Hida an MA (1977) and Doctor of Science (1980) from Kyoto The 2019 Leroy P. Steele Prize for Seminal Contribution to University. He did not have a thesis advisor. He held po- Research is awarded to Haruzo Hida of the University of sitions at Hokkaido University (Japan) from 1977–1987 California, Los Angeles, for his highly original paper “Ga- up to an associate professorship. He visited the Institute for Advanced Study for two years (1979–1981), though he lois representations into GL2(Zp[[X ]]) attached to ordinary cusp forms,” published in 1986 in Inventiones Mathematicae. did not have a doctoral degree in the first year there, and In this paper, Hida made the fundamental discovery the Institut des Hautes Études Scientifiques and Université that ordinary cusp forms occur in p-adic analytic families. de Paris Sud from 1984–1986. Since 1987, he has held a J.-P. Serre had observed this for Eisenstein series, but there full professorship at UCLA (and was promoted to Distin- the situation is completely explicit. The methods and per- guished Professor in 1998). -
Y Nesterov Introductory Lecture Notes on Convex Optimization
Y Nesterov Introductory Lecture Notes On Convex Optimization Jean-Luc filter his minsters discrown thermochemically or undeservingly after Haskel moralised and etymologised terrestrially, thixotropic and ravaging. Funded Artur monopolizes, his screwings mars outboxes sixfold. Maynard is endways: she slave imperiously and planning her underpainting. To provide you signed in this paper we have described as such formulations is a different methodology than the lecture notes on convex optimization nesterov ebook, and prox function minimization in the mapping is based methods 10112015 First part led the lecture notes is available 29102015. Interior gradient ascent algorithms and difficult to note that achieves regret? Comprehensive Exam Syllabus for Continuous Optimization. Convex Optimization for rigorous Science. Register update notification mail Add to favorite lecture list Academic. Nesterov 2003 Introductory Lectures on Convex Optimization Springer. The flow polytope corresponds to note that arise in this. Lecture Notes IE 521 Convex Optimization Niao He. The most important properties of our methods were provided with inequality. Optimization UCLA Computer Science. This is in order information gathered in parallel. CVXPY A Python-Embedded Modeling Language for Convex. The random decision. The assumption that use of an efficient algorithms we note that ogd as well understood and not present can update method clarified on optimization, we assume that learns as labelled data. Introduction to Online Convex Optimization by Elad Hazan Foundations and. Yurii Nesterov Google Cendekia Google Scholar. 3 First order algorithms for online convex optimization 39. Nisheeth K Vishnoi Theoretical Computer Science EPFL. Convergence rates trust-region methods introduction to the AMPL modeling language. Introductory Lectures on Convex Optimization A Basic Course. -
Curriculum Vitæ
Aleksandr M. Kazachkov May 30, 2021 Contact Department of Industrial and Systems Engineering Website akazachk.github.io Information Herbert Wertheim College of Engineering Email akazachkov@ufl.edu 303 Weil Hall, P.O. Box 116595 Office 401B Weil Hall Gainesville, FL 32611-6595, USA Phone +1.352.273.4902 Research Discrete optimization, including theoretical, methodological, and applied aspects, with an emphasis on devel- Interests oping better cutting plane techniques and open-source computational contributions Computational economics, particularly on the fair allocation of indivisible resources and fair mechanism design Social good applications, such as humanitarian logistics and donation distribution Academic University of Florida, Department of Industrial and Systems Engineering, Gainesville, FL, USA Positions Assistant Professor January 2021 – Present Courtesy Assistant Professor March 2020 – December 2020 Polytechnique Montréal, Department of Mathematics and Industrial Engineering, Montréal, QC, Canada Postdoctoral Researcher under Andrea Lodi May 2018 – December 2020 Education Carnegie Mellon University, Tepper School of Business, Pittsburgh, PA, USA Ph.D. in Algorithms, Combinatorics, and Optimization under Egon Balas May 2018 Dissertation: Non-Recursive Cut Generation M.S. in Algorithms, Combinatorics, and Optimization May 2013 Cornell University, College of Engineering, Ithaca, NY, USA B.S. in Operations Research and Engineering with Honors, Magna Cum Laude May 2011 Publications [1] P.I. Frazier and A.M. Kazachkov. “Guessing Preferences: A New Approach to Multi-Attribute Ranking and Selection”, Winter Simulation Conference, 2011. [2] J. Karp, A.M. Kazachkov, and A.D. Procaccia. “Envy-Free Division of Sellable Goods”, AAAI Conference on Artificial Intelligence, 2014. [3] J.P. Dickerson, A.M. Kazachkov, A.D. Procaccia, and T. -
Institute for Pure and Applied Mathematics, UCLA Award/Institution #0439872-013151000 Annual Progress Report for 2009-2010 August 1, 2011
Institute for Pure and Applied Mathematics, UCLA Award/Institution #0439872-013151000 Annual Progress Report for 2009-2010 August 1, 2011 TABLE OF CONTENTS EXECUTIVE SUMMARY 2 A. PARTICIPANT LIST 3 B. FINANCIAL SUPPORT LIST 4 C. INCOME AND EXPENDITURE REPORT 4 D. POSTDOCTORAL PLACEMENT LIST 5 E. INSTITUTE DIRECTORS‘ MEETING REPORT 6 F. PARTICIPANT SUMMARY 12 G. POSTDOCTORAL PROGRAM SUMMARY 13 H. GRADUATE STUDENT PROGRAM SUMMARY 14 I. UNDERGRADUATE STUDENT PROGRAM SUMMARY 15 J. PROGRAM DESCRIPTION 15 K. PROGRAM CONSULTANT LIST 38 L. PUBLICATIONS LIST 50 M. INDUSTRIAL AND GOVERNMENTAL INVOLVEMENT 51 N. EXTERNAL SUPPORT 52 O. COMMITTEE MEMBERSHIP 53 P. CONTINUING IMPACT OF PAST IPAM PROGRAMS 54 APPENDIX 1: PUBLICATIONS (SELF-REPORTED) 2009-2010 58 Institute for Pure and Applied Mathematics, UCLA Award/Institution #0439872-013151000 Annual Progress Report for 2009-2010 August 1, 2011 EXECUTIVE SUMMARY Highlights of IPAM‘s accomplishments and activities of the fiscal year 2009-2010 include: IPAM held two long programs during 2009-2010: o Combinatorics (fall 2009) o Climate Modeling (spring 2010) IPAM‘s 2010 winter workshops continued the tradition of focusing on emerging topics where Mathematics plays an important role: o New Directions in Financial Mathematics o Metamaterials: Applications, Analysis and Modeling o Mathematical Problems, Models and Methods in Biomedical Imaging o Statistical and Learning-Theoretic Challenges in Data Privacy IPAM sponsored reunion conferences for four long programs: Optimal Transport, Random Shapes, Search Engines and Internet MRA IPAM sponsored three public lectures since August. Noga Alon presented ―The Combinatorics of Voting Paradoxes‖ on October 5, 2009. Pierre-Louis Lions presented ―On Mean Field Games‖ on January 5, 2010. -
Polyhedral Outer Approximations in Convex Mixed-Integer Nonlinear
Jan Kronqvist Polyhedral Outer Approximations in Polyhedral Outer Approximations in Convex Mixed-Integer Nonlinear Programming Approximations in Convex Polyhedral Outer Convex Mixed-Integer Nonlinear Programming Jan Kronqvist PhD Thesis in Process Design and Systems Engineering Dissertations published by Process Design and Systems Engineering ISSN 2489-7272 Faculty of Science and Engineering 978-952-12-3734-8 Åbo Akademi University 978-952-12-3735-5 (pdf) Painosalama Oy Åbo 2018 Åbo, Finland 2018 2018 Polyhedral Outer Approximations in Convex Mixed-Integer Nonlinear Programming Jan Kronqvist PhD Thesis in Process Design and Systems Engineering Faculty of Science and Engineering Åbo Akademi University Åbo, Finland 2018 Dissertations published by Process Design and Systems Engineering ISSN 2489-7272 978-952-12-3734-8 978-952-12-3735-5 (pdf) Painosalama Oy Åbo 2018 Preface My time as a PhD student began back in 2014 when I was given a position in the Opti- mization and Systems Engineering (OSE) group at Åbo Akademi University. It has been a great time, and many people have contributed to making these years a wonderful ex- perience. During these years, I was given the opportunity to teach several courses in both environmental engineering and process systems engineering. The opportunity to teach these courses has been a great experience for me. I want to express my greatest gratitude to my supervisors Prof. Tapio Westerlund and Docent Andreas Lundell. Tapio has been a true source of inspiration and a good friend during these years. Thanks to Tapio, I have been able to be a part of an inter- national research environment. -
Prizes and Awards Session
PRIZES AND AWARDS SESSION Wednesday, July 12, 2021 9:00 AM EDT 2021 SIAM Annual Meeting July 19 – 23, 2021 Held in Virtual Format 1 Table of Contents AWM-SIAM Sonia Kovalevsky Lecture ................................................................................................... 3 George B. Dantzig Prize ............................................................................................................................. 5 George Pólya Prize for Mathematical Exposition .................................................................................... 7 George Pólya Prize in Applied Combinatorics ......................................................................................... 8 I.E. Block Community Lecture .................................................................................................................. 9 John von Neumann Prize ......................................................................................................................... 11 Lagrange Prize in Continuous Optimization .......................................................................................... 13 Ralph E. Kleinman Prize .......................................................................................................................... 15 SIAM Prize for Distinguished Service to the Profession ....................................................................... 17 SIAM Student Paper Prizes .................................................................................................................... -
Performance of Optimization Software - an Update
Performance of Optimization Software - an Update INFORMS Annual 2011 Charlotte, NC 13-18 November 2011 H. D. Mittelmann School of Math and Stat Sciences Arizona State University 1 Services we provide • Guide to Software: "Decision Tree" • http://plato.asu.edu/guide.html • Software Archive • Software Evaluation: "Benchmarks" • Archive of Testproblems • Web-based Solvers (1/3 of NEOS) 2 We maintain the following NEOS solvers (8 categories) Combinatorial Optimization * CONCORDE [TSP Input] Global Optimization * ICOS [AMPL Input] Linear Programming * bpmpd [AMPL Input][LP Input][MPS Input][QPS Input] Mixed Integer Linear Programming * FEASPUMP [AMPL Input][CPLEX Input][MPS Input] * SCIP [AMPL Input][CPLEX Input][MPS Input] [ZIMPL Input] * qsopt_ex [LP Input][MPS Input] [AMPL Input] Nondifferentiable Optimization * condor [AMPL Input] Semi-infinite Optimization * nsips [AMPL Input] Stochastic Linear Programming * bnbs [SMPS Input] * DDSIP [LP Input][MPS Input] 3 We maintain the following NEOS solvers (cont.) Semidefinite (and SOCP) Programming * csdp [MATLAB_BINARY Input][SPARSE_SDPA Input] * penbmi [MATLAB Input][MATLAB_BINARY Input] * pensdp [MATLAB_BINARY Input][SPARSE_SDPA Input] * sdpa [MATLAB_BINARY Input][SPARSE_SDPA Input] * sdplr [MATLAB_BINARY Input][SDPLR Input][SPARSE_SDPA Input] * sdpt3 [MATLAB_BINARY Input][SPARSE_SDPA Input] * sedumi [MATLAB_BINARY Input][SPARSE_SDPA Input] 4 Overview of Talk • Current and Selected(*) Benchmarks { Parallel LP benchmarks { MILP benchmark (MIPLIB2010) { Feasibility/Infeasibility Detection benchmarks