Nonlinear Optimization

Markov Decision Process Design: A Framework for Integrating Strategic and Operational Decisions

  • Seth Brown
  • Saumya Sinha
  • Andrew J. Schaefer
    • Categories Integer Programming, Nonlinear Optimization Tags , ,

    We consider the problem of optimally designing a system for repeated use under uncertainty. We develop a modeling framework that integrates design and operational phases, which are represented by a mixed-integer program and discounted-cost infinite-horizon Markov decision processes, respectively. We seek to simultaneously minimize the design costs and the subsequent expected operational costs. This problem … Read more

    A subspace inertial method for derivative-free nonlinear monotone equations

  • Morteza Kimiaei
  • Abdulkarim Hassan Ibrahim
  • Susan Ghaderi
    • Categories Nonlinear Systems and Least-Squares Tags

    We introduce SILSA, a subspace inertial line search algorithm, for finding solutions of nonlinear monotone equations (NME). At each iteration, a new point is generated in a subspace generated by the previous points. Of all finite points forming the subspace, a point with the largest residual norm is replaced by the new point to update … Read more

    Balancing Communication and Computation in Gradient Tracking Algorithms for Decentralized Optimization

  • Albert S. Berahas
  • Raghu Bollapragada
  • Shagun Gupta
    • Categories Convex Optimization, Nonlinear Optimization, Optimization in Data Science Tags , , , ,

    Gradient tracking methods have emerged as one of the most popular approaches for solving decentralized optimization problems over networks. In this setting, each node in the network has a portion of the global objective function, and the goal is to collectively optimize this function. At every iteration, gradient tracking methods perform two operations (steps): (1) … Read more

    Proximal bundle methods for hybrid weakly convex composite optimization problems

  • Jiaming Liang
  • Renato D.C. Monteiro
  • HonghaoZhang
    • Categories Nonlinear Optimization, Nonsmooth Optimization Tags , , , ,

    This paper establishes the iteration-complexity of proximal bundle methods for solving hybrid (i.e., a blend of smooth and nonsmooth) weakly convex composite optimization (HWC-CO) problems. This is done in a unified manner by considering a proximal bundle framework (PBF) based on a generic bundle update scheme which includes various well-known bundle update schemes. In contrast … 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

    Test Instances for Multiobjective Mixed-Integer Nonlinear Optimization

  • Leo Warnow
  • Gabriele Eichfelder
  • Tobias Gerlach
    • Categories (Mixed) Integer Nonlinear Programming, Multi-Criteria Optimization, Nonlinear Optimization Tags , , ,

    A suitable set of test instances, also known as benchmark problems, is a key ingredient to systematically evaluate numerical solution algorithms for a given class of optimization problems. While in recent years several solution algorithms for the class of multiobjective mixed-integer nonlinear optimization problems have been proposed, there is a lack of a well-established set … Read more

    An Explicit Three-Term Polak-Ribière-Polyak Conjugate Gradient Method for Bicriteria Optimization

  • Mounir El Maghri
  • Youssef Elboulqe
    • Categories Multi-Criteria Optimization, Unconstrained Optimization Tags , , , , ,

    We propose in this paper a Polak-Ribière-Polyak conjugate gradient type method for solving bicriteria optimization problems by avoiding scalarization techniques. Two particular advantages in this contribution are to be noted. First, the suggested descent direction common to both criteria may be directly computed by a given formula without solving any intermediate subproblem. Second, the descent … Read more

    Solving low-rank semidefinite programs via manifold optimization

  • Jie Wang
    • Categories Constrained Nonlinear Optimization, Optimization Software Benchmark, Semi-definite Programming Tags , , , , ,

    We propose a manifold optimization approach to solve linear semidefinite programs (SDP) with low-rank solutions. This approach incorporates the augmented Lagrangian method and the Burer-Monteiro factorization, and features the adaptive strategies for updating the factorization size and the penalty parameter. We prove that the present algorithm can solve SDPs to global optimality, despite of the … Read more

    On a Frank-Wolfe Approach for Abs-smooth Functions

  • Timo Kreimeier
  • Sebastian Pokutta
  • Andrea Walther
  • Zev Woodstock
    • Categories Constrained Nonlinear Optimization, Nonsmooth Optimization, Other Topics Tags , , , ,

    We propose an algorithm which appears to be the first bridge between the fields of conditional gradient methods and abs-smooth optimization. Our problem setting is motivated by various applications that lead to nonsmoothness, such as $\ell_1$ regularization, phase retrieval problems, or ReLU activation in machine learning. To handle the nonsmoothness in our problem, we propose … 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