Integer Programming

Exact Methods for Discrete Γ-Robust Interdiction Problems with an Application to the Bilevel Knapsack Problem

  • Yasmine Beck
  • Ivana Ljubić
  • Martin Schmidt
    • Categories (Mixed) Integer Nonlinear Programming, Cutting Plane Approaches, Robust Optimization Tags , , , ,

    Developing solution methods for discrete bilevel problems is known to be a challenging task – even if all parameters of the problem are exactly known. Many real-world applications of bilevel optimization, however, involve data uncertainty. We study discrete min-max problems with a follower who faces uncertainties regarding the parameters of the lower-level problem. Adopting a … Read more

    Mixed-Integer Optimization with Constraint Learning

  • Donato Maragno
  • Holly Wiberg
  • Dimitris Bertsimas
  • Ş. İlker Birbil
  • Dick den Hertog
  • Adejuyigbe Fajemisin
    • Categories (Mixed) Integer Linear Programming, Applications - OR and Management Sciences, Data-Mining Tags , , ,

    We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which constraints and objectives are directly learned from data using machine learning, and the trained models are embedded in an optimization formulation. We exploit the mixed-integer optimization-representability of many machine learning methods, including … Read more

    Exact and Heuristic Solution Techniques for Mixed-Integer Quantile Minimization Problems

  • Diego Cattaruzza
  • Martine Labbé
  • Matteo Petris
  • Marius Roland
  • Martin Schmidt
    • Categories (Mixed) Integer Linear Programming, Applications - Science and Engineering, Stochastic Programming Tags , , , ,

    We consider mixed-integer linear quantile minimization problems that yield large-scale problems that are very hard to solve for real-world instances. We motivate the study of this problem class by two important real-world problems: a maintenance planning problem for electricity networks and a quantile-based variant of the classic portfolio optimization problem. For these problems, we develop … Read more

    Simple odd beta-cycle inequalities for binary polynomial optimization

  • Alberto Del Pia
  • Matthias Walter
    • Categories 0-1 Programming, Bound-constrained Optimization, Polyhedra Tags , ,

    We consider the multilinear polytope which arises naturally in binary polynomial optimization. Del Pia and Di Gregorio introduced the class of odd beta-cycle inequalities valid for this polytope, showed that these generally have Chvátal rank 2 with respect to the standard relaxation and that, together with flower inequalities, they yield a perfect formulation for cycle … Read more

    Compact extended formulations for low-rank functions with indicator variables

  • Shaoning Han
  • Andrés Gómez
    • Categories (Mixed) Integer Nonlinear Programming Tags , , ,

    We study the mixed-integer epigraph of a special class of convex functions with non-convex indicator constraints, which are often used to impose logical constraints on the support of the solutions. The class of functions we consider are defined as compositions of low-dimensional nonlinear functions with affine functions Extended formulations describing the convex hull of such … Read more

    A Graph-based Decomposition Method for Convex Quadratic Optimization with Indicators

  • Peijing Liu
  • Salar Fattahi
  • Andrés Gómez
  • Simge Küçükyavuz
    • Categories (Mixed) Integer Nonlinear Programming, Statistics Tags , , , , , ,

    In this paper, we consider convex quadratic optimization problems with indicator variables when the matrix Q defining the quadratic term in the objective is sparse. We use a graphical representation of the support of Q, and show that if this graph is a path, then we can solve the associated problem in polynomial time. This … Read more

    A Branch & Bound Algorithm for Robust Binary Optimization with Budget Uncertainty

  • Christina Büsing
  • Timo Gersing
  • Arie M.C.A. Koster
    • Categories 0-1 Programming, Branch and Cut Algorithms, Robust Optimization Tags , , , ,

    Since its introduction in the early 2000s, robust optimization with budget uncertainty has received a lot of attention. This is due to the intuitive construction of the uncertainty sets and the existence of a compact robust reformulation for (mixed-integer) linear programs. However, despite its compactness, the reformulation performs poorly when solving robust integer problems due … Read more

    An Overview of Nested Decomposition for Multi-Level Optimization Problems

  • Stephen J. Maher
  • Ibrahim Muter
    • Categories Applications - OR and Management Sciences, Combinatorial Optimization, Integer Programming Tags , , , ,

    Nested multi-level structures are frequently encountered in many real-world optimization problems. Decomposition techniques are a commonly applied approach used to handle nested multi-level structures; however, the typical problem-specific focus of such techniques has led to numerous specialized formulations and solution methods. This lack of generalized results for nested multi-level optimization problems is addressed in this … Read more

    Efficient and Robust Mixed-Integer Optimization Methods for Training Binarized Deep Neural Networks

  • Jannis Kurtz
  • Bubacarr Bah
    • Categories Applications - OR and Management Sciences, Integer Programming, Robust Optimization Tags ,

    Compared to classical deep neural networks its binarized versions can be useful for applications on resource-limited devices due to their reduction in memory consumption and computational demands. In this work we study deep neural networks with binary activation functions and continuous or integer weights (BDNN). We show that the BDNN can be reformulated as a … Read more

    A Theoretical and Computational Analysis of Full Strong-Branching

  • Santanu S. Dey
  • Yatharth Dubey
  • Marco Molinaro
  • Prachi Shah
    • Categories Branch and Cut Algorithms, Integer Programming Tags , , ,

    Full strong-branching (henceforth referred to as strong-branching) is a well-known variable selection rule that is known experimentally to produce significantly smaller branch-and-bound trees in comparison to all other known variable selection rules. In this paper, we attempt an analysis of the performance of the strong-branching rule both from a theoretical and a computational perspective. On … Read more