INFORMS OPTIMIZATION SOCIETY Business Meeting
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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. -
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). -
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. -
Algorithmic System Design Under Consideration of Dynamic Processes
Algorithmic System Design under Consideration of Dynamic Processes Vom Fachbereich Maschinenbau an der Technischen Universit¨atDarmstadt zur Erlangung des akademischen Grades eines Doktors der Ingenieurwissenschaften (Dr.-Ing.) genehmigte Dissertation vorgelegt von Dipl.-Phys. Lena Charlotte Altherr aus Karlsruhe Berichterstatter: Prof. Dr.-Ing. Peter F. Pelz Mitberichterstatter: Prof. Dr. rer. nat. Ulf Lorenz Tag der Einreichung: 19.04.2016 Tag der m¨undlichen Pr¨ufung: 24.05.2016 Darmstadt 2016 D 17 Vorwort des Herausgebers Kontext Die Produkt- und Systementwicklung hat die Aufgabe technische Systeme so zu gestalten, dass eine gewünschte Systemfunktion erfüllt wird. Mögliche System- funktionen sind z.B. Schwingungen zu dämpfen, Wasser in einem Siedlungsgebiet zu verteilen oder die Kühlung eines Rechenzentrums. Wir Ingenieure reduzieren dabei die Komplexität eines Systems, indem wir dieses gedanklich in überschaubare Baugruppen oder Komponenten zerlegen und diese für sich in Robustheit und Effizienz verbessern. In der Kriegsführung wurde dieses Prinzip bereits 500 v. Chr. als „Teile und herrsche Prinzip“ durch Meister Sun in seinem Buch „Die Kunst der Kriegsführung“ beschrieben. Das Denken in Schnitten ist wesentlich für das Verständnis von Systemen: „Das wichtigste Werkzeug des Ingenieurs ist die Schere“. Das Zusammenwirken der Komponenten führt anschließend zu der gewünschten Systemfunktion. Während die Funktion eines technischen Systems i.d.R. nicht verhan- delbar ist, ist jedoch verhandelbar mit welchem Aufwand diese erfüllt wird und mit welcher Verfügbarkeit sie gewährleistet wird. Aufwand und Verfügbarkeit sind dabei gegensätzlich. Der Aufwand bemisst z.B. die Emission von Kohlendioxid, den Energieverbrauch, den Materialverbrauch, … die „total cost of ownership“. Die Verfügbarkeit bemisst die Ausfallzeiten, Lebensdauer oder Laufleistung. Die Gesell- schaft stellt sich zunehmend die Frage, wie eine Funktion bei minimalem Aufwand und maximaler Verfügbarkeit realisiert werden kann. -
54 OP08 Abstracts
54 OP08 Abstracts CP1 Dept. of Mathematics Improving Ultimate Convergence of An Aug- [email protected] mented Lagrangian Method Optimization methods that employ the classical Powell- CP1 Hestenes-Rockafellar Augmented Lagrangian are useful A Second-Derivative SQP Method for Noncon- tools for solving Nonlinear Programming problems. Their vex Optimization Problems with Inequality Con- reputation decreased in the last ten years due to the com- straints parative success of Interior-Point Newtonian algorithms, which are asymptotically faster. In the present research We consider a second-derivative 1 sequential quadratic a combination of both approaches is evaluated. The idea programming trust-region method for large-scale nonlin- is to produce a competitive method, being more robust ear non-convex optimization problems with inequality con- and efficient than its ”pure” counterparts for critical prob- straints. Trial steps are composed of two components; a lems. Moreover, an additional hybrid algorithm is defined, Cauchy globalization step and an SQP correction step. A in which the Interior Point method is replaced by the New- single linear artificial constraint is incorporated that en- tonian resolution of a KKT system identified by the Aug- sures non-accent in the SQP correction step, thus ”guiding” mented Lagrangian algorithm. the algorithm through areas of indefiniteness. A salient feature of our approach is feasibility of all subproblems. Ernesto G. Birgin IME-USP Daniel Robinson Department of Computer Science Oxford University [email protected] -
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 .................................................................................................................... -
Bcol Research Report 14.02
BCOL RESEARCH REPORT 14.02 Industrial Engineering & Operations Research University of California, Berkeley, CA 94720-1777 Forthcoming in Discrete Optimization SUPERMODULAR COVERING KNAPSACK POLYTOPE ALPER ATAMTURK¨ AND AVINASH BHARDWAJ Abstract. The supermodular covering knapsack set is the discrete upper level set of a non-decreasing supermodular function. Submodular and supermodular knapsack sets arise naturally when modeling utilities, risk and probabilistic constraints on discrete variables. In a recent paper Atamt¨urkand Narayanan [6] study the lower level set of a non-decreasing submodular function. In this complementary paper we describe pack inequalities for the super- modular covering knapsack set and investigate their separation, extensions and lifting. We give sequence-independent upper bounds and lower bounds on the lifting coefficients. Furthermore, we present a computational study on using the polyhedral results derived for solving 0-1 optimization problems over conic quadratic constraints with a branch-and-cut algorithm. Keywords : Conic integer programming, supermodularity, lifting, probabilistic cov- ering knapsack constraints, branch-and-cut algorithms Original: September 2014, Latest: May 2015 This research has been supported, in part, by grant FA9550-10-1-0168 from the Office of Assistant Secretary Defense for Research and Engineering. 2 ALPER ATAMTURK¨ AND AVINASH BHARDWAJ 1. Introduction A set function f : 2N ! R is supermodular on finite ground set N [22] if f(S) + f(T ) ≤ f(S [ T ) + f(S \ T ); 8 S; T ⊆ N: By abuse of notation, for S ⊆ N we refer to f(S) also as f(χS), where χS denotes the binary characteristic vector of S. Given a non-decreasing supermodular function f on N and d 2 R, the supermodular covering knapsack set is defined as n o K := x 2 f0; 1gN : f(x) ≥ d ; that is, the discrete upper level set of f. -
Data-Driven Methods and Applications for Optimization Under Uncertainty and Rare-Event Simulation
Data-Driven Methods and Applications for Optimization under Uncertainty and Rare-Event Simulation by Zhiyuan Huang A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Industrial and Operations Engineering) in The University of Michigan 2020 Doctoral Committee: Assistant Professor Ruiwei Jiang, Co-chair Associate Professor Henry Lam, Co-chair Associate Professor Eunshin Byon Assistant Professor Gongjun Xu Assistant Professor Ding Zhao Zhiyuan Huang [email protected] ORCID iD: 0000-0003-1284-2128 c Zhiyuan Huang 2020 Dedicated to my parents Weiping Liu and Xiaolei Huang ii ACKNOWLEDGEMENTS First of all, I am deeply thankful to my advisor Professor Henry Lam for his guidance and support throughout my Ph.D. experience. It has been one of the greatest honor of my life working with him as a graduate student. His wisdom, kindness, dedication, and passion have encouraged me to overcome difficulties and to complete my dissertation. I also would like to thank my dissertation committee members. I want to thank the committee co-chair Professor Ruiwei Jiang for his support in my last two Ph.D. years. I am also grateful to Professor Ding Zhao for his consistent help and collaboration. My appreciation also goes to Professor Gongjun Xu for his valuable comments on my dissertation. I would like to thank Professor Eunshin Byon for being helpful both academically and personally. Next, I want to thank other collaborators in my Ph.D. studies. I am very fortunate to encounter great mentor and collaborator Professor Jeff Hong, who made great advice on Chapter 2 of this dissertation. -
Norbert Wiener Prizes in Applied Mathematics
FROM THE AMS SECRETARY 2019 Norbert Wiener Prizes in Applied Mathematics The 2019 Norbert Wiener Prizes in Applied Mathematics were presented at the 125th Annual Meeting of the AMS in Baltimore, Maryland, in January 2019. The prizes were awarded to MARSHA BERGER and to ARKADI NEMIROVSKI. Citation: Marsha Berger to simulate tsunamis, debris flows, and dam breaks, among The 2019 Norbert Wiener Prize other applications. in Applied Mathematics is Biographical Note: Marsha Berger awarded to Marsha Berger for her fundamental contributions Marsha Berger received her PhD in computer science from to Adaptive Mesh Refinement Stanford in 1982. She started as a postdoc at the Courant Institute of Mathematical Sciences at NYU, and is currently and to Cartesian mesh tech- a Silver Professor in the computer science department, niques for automating the sim- where she has been since 1985. ulation of compressible flows She is a frequent visitor to NASA Ames, where she has in complex geometry. spent every summer since 1990 and several sabbaticals. Her In solving partial differen- honors include membership in the National Academy of tial equations, Adaptive Mesh Sciences, the National Academy of Engineering, and the Marsha Berger Refinement (AMR) algorithms American Academy of Arts and Science. She is a fellow of can improve the accuracy of a the Society for Industrial and Applied Mathematics. Berger solution by locally and dynam- was a recipient of the Institute of Electrical and Electronics ically resolving complex features of a simulation. Marsha Engineers Fernbach Award and was part of the team that Berger is one of the inventors of AMR. -
Mathematics People
NEWS Mathematics People Tardos Named Goldfarb and Nocedal Kovalevsky Lecturer Awarded 2017 von Neumann Éva Tardos of Cornell University Theory Prize has been chosen as the 2018 AWM- SIAM Sonia Kovalevsky Lecturer by Donald Goldfarb of Columbia the Association for Women in Math- University and Jorge Nocedal of ematics (AWM) and the Society for Northwestern University have been Industrial and Applied Mathematics awarded the 2017 John von Neu- (SIAM). She was honored for her “dis- mann Theory Prize by the Institute tinguished scientific contributions for Operations Research and the to the efficient methods for com- Management Sciences (INFORMS). binatorial optimization problems According to the prize citation, “The Éva Tardos on graphs and networks, and her award recognizes the seminal con- work on issues at the interface of tributions that Donald Goldfarb computing and economics.” According to the prize cita- Jorge Nocedal and Jorge Nocedal have made to the tion, she is considered “one of the leaders in defining theory and applications of nonlinear the area of algorithmic game theory, in which algorithms optimization over the past several decades. These contri- are designed in the presence of self-interested agents butions cover a full range of topics, going from modeling, governed by incentives and economic constraints.” With to mathematical analysis, to breakthroughs in scientific Tim Roughgarden, she was awarded the 2012 Gödel Prize computing. Their work on the variable metric methods of the Association for Computing Machinery (ACM) for a paper “that shaped the field of algorithmic game theory.” (BFGS and L-BFGS, respectively) has been extremely in- She received her PhD in 1984 from Eötvös Loránd Univer- fluential.” sity. -
Basic Polyhedral Theory 3
BASIC POLYHEDRAL THEORY VOLKER KAIBEL A polyhedron is the intersection of finitely many affine halfspaces, where an affine halfspace is a set ≤ n H (a, β)= x Ê : a, x β { ∈ ≤ } n n n Ê Ê for some a Ê and β (here, a, x = j=1 ajxj denotes the standard scalar product on ). Thus, every∈ polyhedron is∈ the set ≤ n P (A, b)= x Ê : Ax b { ∈ ≤ } m×n of feasible solutions to a system Ax b of linear inequalities for some matrix A Ê and some m ≤ n ′ ′ ∈ Ê vector b Ê . Clearly, all sets x : Ax b, A x = b are polyhedra as well, as the system A′x = b∈′ of linear equations is equivalent{ ∈ the system≤ A′x }b′, A′x b′ of linear inequalities. A bounded polyhedron is called a polytope (where bounded≤ means− that≤ there − is a bound which no coordinate of any point in the polyhedron exceeds in absolute value). Polyhedra are of great importance for Operations Research, because they are not only the sets of feasible solutions to Linear Programs (LP), for which we have beautiful duality results and both practically and theoretically efficient algorithms, but even the solution of (Mixed) Integer Linear Pro- gramming (MILP) problems can be reduced to linear optimization problems over polyhedra. This relationship to a large extent forms the backbone of the extremely successful story of (Mixed) Integer Linear Programming and Combinatorial Optimization over the last few decades. In Section 1, we review those parts of the general theory of polyhedra that are most important with respect to optimization questions, while in Section 2 we treat concepts that are particularly relevant for Integer Programming. -
1. Introduction: Learning About Operations Research
1. Introduction: Learning about Operations Research IRV LUSTIG: So hi, my name's Irv Lustig from Princeton Consultants and today, I'm privileged to interview George Nemhauser, who is currently a distinguished professor at the Georgia Institute of Technology. So thank you, George, for joining us today and let's get started. GEORGE NEMHAUSER: Oh, you're most welcome. I'm really looking forward to it. IRV LUSTIG: All right. So tell me, when did you first learn about operations research? GEORGE NEMHAUSER: So basically, I am a chemical engineer. And I think it was the summer between when I graduated with a Bachelor's degree in Chemical Engineering and the fall, when I was going to go off to graduate school at Northwestern for Chemical Engineering, I had a job with the research department of Allied Chemical. And there was a guy-- I think he was my supervisor who was working there-- who told me he was taking a part-time degree at Princeton and he was studying things like linear programming and game theory and I think maybe Kuhn, Tucker conditions, And he told me about this kind of stuff and I thought, wow, this is really interesting stuff, much more interesting than what I'm doing in chemical engineering. So when I got to Northwestern, as a first-year graduate student in Chemical Engineering, I had one elective course. And I looked and there was this new course called Operations Research and it included linear programming and game theory and stuff like that. I said, that's it.