INFORMS Open Forum

Winners of the 2020 INFORMS Optimization Society Prizes

  • 1.  Winners of the 2020 INFORMS Optimization Society Prizes

    Posted 12 days ago

    The Khachiyan Prize winners, the Farkas Prize winner, the inaugural Egon Balas Prize winner, the winners of the Young Researchers Prize, and the winner of the Student Paper Prize will present their work at the INFORMS Annual Meeting in November 9, 2020, 12:30pm-1:45 pm. Please read on to learn the winners. 

    2020 INFORMS Optimization Society Khachiyan Prize

    WINNERS: Stephen J. Wright (Computer Science, University of Wisconsin at Madison) and James B. Orlin (Sloan School of Management, MIT)

    Citation for Stephen J. Wright: For his vast contributions to continuous optimization, spanning theory, algorithms and software, and the impact of his work on control, signal processing, and machine learning.

    Citation for James B. Orlin: For his deep and extensive contributions to network flows and combinatorial optimization, including novel results for max flows, min cost flows, min cuts, and shortest cycles.

    2020 INFORMS Optimization Society Farkas Prize

    WINNER: Jesus De Loera (Mathematics, University of California, Davis)

    The INFORMS Optimization Society 2020 Farkas Prize is awarded to Jesus De Loera in recognition of his pioneering work at the intersection of discrete mathematics, optimization and algebraic geometry. Professor De Loera's research includes fundamental results on topics including the complexity of interior-point methods for linear programming, the Hirsch conjecture for network flow polytopes, use of Gröbner and Graver bases for discrete optimization and the theory and application of triangulations.  His research is noted for the successful computational implementation of methods based on complex mathematical theory, including the enumeration of lattice points in polyhedra and the application of Hilbert's Nullstellensatz to combinatorial optimization.  Professor De Loera is also well known as an outstanding expositor and mentor whose infectious enthusiasm draws other researchers to work in areas where they might otherwise not dare to venture.


    2020 INFORMS Optimization Society Egon Balas Prize

    WINNER: Santanu S. Dey (Industrial and Systems Engineering, Georgia Tech)

    The inaugural IOS Balas Prize is awarded to Professor Santanu S. Dey of Georgia Tech for broad and significant contributions to the theory, methodology, and applications of Discrete Optimization. Professor Dey made strong and seminal contributions to the theory of maximal lattice-free convex sets, multi-row cuts, sparse cutting planes in integer programming, the structure of mixed-integer convex optimization, and along with co-authors, developed practical algorithms for power problems based on strong relaxations from a detailed analysis of the underlying systems.

    2020 INFORMS Optimization Society Young Researchers Paper Prize

    WINNER: Hussein Hazimeh and Rahul Mazumder (Operations Research Center, MIT)

    Hussein Hazimeh and Rahul Mazumder are awarded the 2020 INFORMS Optimization Society Prize for Young Researchers for their paper, "Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms," Operations Research, to appear. The paper
    presents a novel algorithm for the Best Subset Selection problem (BSS), which is ordinary least-squares linear regression but with a penalty (or constraint) on the number of non-zero coefficients in the model, which induces sparsity. BSS is a fundamental problem in high-dimensional statistics, where sparse solutions with good explanatory power are crucial for practical use. Yet, it is NP-hard and, therefore, has been labeled computationally intractable and replaced by popular formulations such as LASSO and ridge regression. The authors combine tools and techniques from high-dimensional statistics, continuous optimization, integer programming, and open-source software development to provide the community with a highly effective heuristic for BSS, one which has strong theoretical underpinnings, outperforms existing state-of-the-art codes in many cases, and is easy to generalize to other settings.

    WINNER: Weijun Xie (Industrial and Systems Engineering, Virginia Tech)

    Weijun Xie is awarded the 2020 INFORMS Optimization Society Prize for Young Researchers for his paper, "On Distributionally Robust Chance Constrained Programs with Wasserstein Distance," Mathematical Programming (Series A), to appear. The paper studies the
    distributionally robust chance constrained program specifically with Wasserstein ambiguity set (DRCCP-W), i.e., the variable probability distribution is modeled using the Wasserstein distance from a given, empirical distribution. Compared with prior studies, which have used
    other types of ambiguity sets, the current paper settles the open question as to whether DRCCP with the highly desirable Wasserstein set can be optimized by means of a computable reformulation. Specifically, the paper shows: a conditional-value-at-risk formulation of DRCCP-W that allows tighter inner and outer approximations; a big-M mixed-integer formulation when the feasible region is bounded; and also a big-M-free formulation when the decision variables are binary. The paper also includes a careful numerical study supporting the effectiveness of the
    various formulations.

    2020 INFORMS Optimization Society Student Paper Prizes

    Yingjie Bi (Electrical and Computer Engineering Cornell University)

    Citation: This paper titled "Duality gap estimation via a refined Shapley-Folkman lemma" (jointly authored with A. Kevin Tang) addresses an important question in optimization: estimating the duality gap for optimization problems with a nonconvex, separable objective. This problem has classical roots based on the Shapley-Folkman lemma from the 1950s. Yet, the best known gap estimates go back to the 1980s, with the exception of a 2016 paper (by M. Udell and S. Boyd) which improves the coefficients but not the order of the gap estimate.  In this paper, by introducing new concepts, such as the k-th convex hull, the authors are able to refine the Shapley-Folkman lemma considerably. Their geometric approach to this problem coupled with convex analysis tools not only provides new insights into this classical problem, but also allows for elegant derivations and clean results that significantly improve the best known duality gap estimates. Their results also extend to the setting of nonconvex constraints, an equally important yet much less studied problem class. They substantiate many of their insights through convincing applications in engineering, e.g., internet routing, power control in communications, for which this paper improves upon the best known gap bounds in the literature. Collectively, this paper paves the way for a novel understanding of duality gap estimation of nonconvex separable optimization problems. We expect this work to endure and to find exciting applications in domains including operations research, machine learning, and engineering where nonconvexities are prevalent.


    Digivijay Boob (Industrial and Systems Engineering, Georgia Institute of Technology) for the paper "Stochastic First-order Methods for Convex and Nonconvex Function Constrained Optimization" (jointly authored with George Lan and Qi Deng)

    Warmest congratulations!

    Andy Sun

    IOS Secretary/Treasurer
    Associate Professor
    Georgia Institute of Technology
    Atlanta, GA, USA