Bound-constrained Optimization

Algorithm xxx: APPSPACK 4.0: Asynchronous Parallel Pattern Search for Derivative-Free Optimization

  • Genetha Gray
  • Tamara Kolda
    • Categories Bound-constrained Optimization, Optimization Software and Modeling Systems, Parallel Algorithms Tags , , ,

    APPSPACK is software for solving unconstrained and bound constrained optimization problems. It implements an asynchronous parallel pattern search method that has been specifically designed for problems characterized by expensive function evaluations. Using APPSPACK to solve optimization problems has several advantages: No derivative information is needed; the procedure for evaluating the objective function can be executed … Read more

    Sensitivity of trust-region algorithms on their parameters

  • Nicholas I. M. Gould
  • Dominique Orban
  • Annick Sartenaer
  • Philippe L. Toint
    • Categories Bound-constrained Optimization, Unconstrained Optimization Tags , ,

    In this paper, we examine the sensitivity of trust-region algorithms on the parameters related to the step acceptance and update of the trust region. We show, in the context of unconstrained programming, that the numerical efficiency of these algorithms can easily be improved by choosing appropriate parameters. Recommanded ranges of values for these parameters are … Read more

    A NEW SELF-CONCORDANT BARRIER FOR THE HYPERCUBE

  • Paulo Roberto Oliveira
  • Erik Alex Papa Quiroz
    • Categories Bound-constrained Optimization, Linear Programming Tags ,

    In this paper we introduce a new barrier function $\sum\limits_{i=1}^n(2x_i-1)[\ln{x_i}-\ln(1-x_i)]$ to solve the following optimization problem: $\min\,\, f(x)$ subject to: $Ax=b;\;\;0\leq x\leq e$. We show that this function is a $(3/2)n$-self-concordant barrier on the hypercube $[0,1]^n$. We prove that the central path is well defined and that under an additional assumption on the objective function, … Read more

    Symbolic-interval heuristic for bound-constrained minimization

  • Evgueni Petrov
    • Categories Bound-constrained Optimization Tags

    Bound-constrained global optimization helps answer many practical questions in chemistry, molecular biology, economics. Most of algorithms for solution of global optimization problems are a combination of interval methods and exhuastive search. The efficiency of such algorithms is characterized by their ability to detect and eliminate sub-optimal feasible regions. This ability is increased by availability of … Read more

    Pattern search algorithms for mixed variable programming

  • Charles Audet
  • John E. Dennis, Jr.
    • Categories (Mixed) Integer Nonlinear Programming, Bound-constrained Optimization Tags , , , ,

    Many engineering optimization problems involve a special kind of discrete variable that {\em can} be represented by a number, but this representation has no significance. Such variables arise when a decision involves some situation like a choice from an unordered list of categories. This has two implications: The standard approach of solving problems with continuous … Read more

    Mixed variable optimization of the number and composition of heat intercepts in a thermal insulation system

  • Charles Audet
  • John E. Dennis, Jr.
  • Michael Kokkolaras
    • Categories (Mixed) Integer Nonlinear Programming, Bound-constrained Optimization, Mechanical Engineering Tags , , , , ,

    In the literature, thermal insulation systems with a fixed number of heat intercepts have been optimized with respect to intercept locations and temperatures. The number of intercepts and the types of insulators that surround them were chosen by parametric studies. This was because the optimization methods used could not treat such categorical variables. Discrete optimization … Read more

    An infeasible active set method for convex problems with simple bounds

  • Franz Rendl
  • Karl Kunisch
    • Categories Bound-constrained Optimization Tags ,

    A primal-dual active set method for convex quadratic problems with bound constraints is presented. Based on a guess on the active set, a primal-dual pair $(x,s)$ is computed that satisfies the first order optimality condition and the complementarity condition. If $(x,s)$ is not feasible, a new active set is determined, and the process is iterated. … Read more