Global Optimization

Optimal Low-Rank Matrix Completion: Semidefinite Relaxations and Eigenvector Disjunctions

  • Dimitris Bertsimas
  • Ryan Cory-Wright
  • Sean Lo
  • Jean Pauphilet
    • Categories Data-Mining, Global Optimization Theory, Semi-definite Programming

    Low-rank matrix completion consists of computing a matrix of minimal complexity that recovers a given set of observations as accurately as possible. Unfortunately, existing methods for matrix completion are heuristics that, while highly scalable and often identifying high-quality solutions, do not possess any optimality guarantees. We reexamine matrix completion with an optimality-oriented eye. We reformulate … Read more

    Heuristic methods for noisy derivative-free bound-constrained mixed-integer optimization

  • Morteza Kimiaei
  • Arnold Neumaier
    • Categories (Mixed) Integer Nonlinear Programming, Bound-constrained Optimization, Global Optimization Tags

    This paper introduces MATRS, a novel matrix adaptation trust-region strategy designed to solve noisy derivative-free mixed-integer optimization problems with simple bounds in low dimensions. MATRS operates through a repeated cycle of five phases: mutation, selection, recombination, trust-region, and mixed-integer, executed in this sequence. But if in the mutation phase a new best point (the point … Read more

    Polyhedral Properties of RLT Relaxations of Nonconvex Quadratic Programs and Their Implications on Exact Relaxations

  • Yuzhou Qiu
  • E. Alper Yildirim
    • Categories Global Optimization, Linear Programming, Quadratic Programming Tags , , ,

    We study linear programming relaxations of nonconvex quadratic programs given by the reformulation-linearization technique (RLT), referred to as RLT relaxations. We investigate the relations between the polyhedral properties of the feasible regions of a quadratic program and its RLT relaxation. We establish various connections between recession directions, boundedness, and vertices of the two feasible regions. … Read more

    Convergence analysis on a data-driven inexact proximal-indefinite stochastic ADMM

  • Jianchao Bai
    • Categories Convex Optimization, Stochastic Approaches Tags , , , , ,

    In this paper, we propose an Inexact Proximal-indefinite Stochastic ADMM (abbreviated as IPS-ADMM) to solve a class of separable convex optimization problems whose objective functions consist of two parts: one is an average of many smooth convex functions and the other is a convex but potentially nonsmooth function. The involved smooth subproblem is tackled by … Read more

    (ε-)Efficiency in Fractional Vector Optimization

  • Mounir El Maghri
    • Categories Convex and Nonsmooth Optimization, Global Optimization, Multi-Criteria Optimization Tags , , , ,

    The issue of characterizing completely efficient (Pareto) solutions to a fractional vector (multiobjective or multicriteria) minimization problem, where the involved functions are convex, has not been addressed previously. Thanks to an earlier characterization of weak efficiency in difference vector optimization by El Maghri, we get a vectorial necessary and sufficient condition given in terms of … Read more

    Effective matrix adaptation strategy for noisy derivative-free optimization

  • Morteza Kimiaei
  • Arnold Neumaier
    • Categories Global Optimization Applications, Nonlinear Optimization, Optimization Software Benchmark Tags

    In this paper, we introduce a new effective matrix adaptation evolution strategy (MADFO) for noisy derivative-free optimization problems. Like every MAES solver, MADFO consists of three phases: mutation, selection and recombination. MADFO improves the mutation phase by generating good step sizes, neither too small nor too large, that increase the probability of selecting mutation points … Read more

    Equivalent Sufficient Conditions for Global Optimality of Quadratically Constrained Quadratic Program

  • Sunyoung Kim
  • Masakazu Kojima
    • Categories Global Optimization, Linear, Cone and Semidefinite Programming, Semi-definite Programming Tags , , , , ,

    We study the equivalence of several well-known sufficient optimality conditions for a general quadratically constrained quadratic program (QCQP). The conditions are classified in two categories. The first one is for determining an optimal solution and the second one is for finding an optimal value. The first category of conditions includes the existence of a saddle … Read more

    Nonexpansive Markov Operators and Random Function Iterations for Stochastic Fixed Point Problems

  • Russell Luke
    • Categories Global Optimization Theory, Stochastic Approaches, Stochastic Programming Tags , ,

    We study the convergence of random function iterations for finding an invariant measure of the corresponding Markov operator. We call the problem of finding such an invariant mea- sure the stochastic fixed point problem. This generalizes earlier work studying the stochastic feasibility problem, namely, to find points that are, with probability 1, fixed points of … Read more

    A Radial Basis Function Method for Noisy Global Optimisation

  • Dirk Banholzer
  • Jörg Fliege
  • Ralf Werner
    • Categories Global Optimization, Global Optimization Theory

    We present a novel response surface method for global optimisation of an expensive and noisy (black-box) objective function, where error bounds on the deviation of the observed noisy function values from their true counterparts are available. The method is based on the well-established RBF method by Gutmann (2001a,c) for minimising an expensive and deterministic objective … Read more

    On Exact and Inexact RLT and SDP-RLT Relaxations of Quadratic Programs with Box Constraints

  • Yuzhou Qiu
  • E. Alper Yildirim
    • Categories Bound-constrained Optimization, Global Optimization, Quadratic Programming Tags , , ,

    Quadratic programs with box constraints involve minimizing a possibly nonconvex quadratic function subject to lower and upper bounds on each variable. This is a well-known NP-hard problem that frequently arises in various applications. We focus on two convex relaxations, namely the RLT (Reformulation-Linearization Technique) relaxation and the SDP-RLT relaxation obtained by adding semidefinite constraints to … Read more