derivative-free optimization

A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department

  • Alberto De Santis
  • Tommaso Giovannelli
  • Stefano Lucidi
  • Mauro Messedaglia
  • Massimo Roma
    • Categories Applications - OR and Management Sciences, Integer Programming, Optimization of Simulated Systems Tags , , , ,

    Accurate modeling of the patient flow within an Emergency Department (ED) is required by all studies dealing with the increasing and well-known problem of overcrowding. Since Discrete Event Simulation (DES) models are often adopted with the aim of assessing solutions for reducing the impact of this worldwide phenomenon, an accurate estimation of the service time … Read more

    Constrained stochastic blackbox optimization using a progressive barrier and probabilistic estimates

  • Kwassi Joseph Dzahini
  • Michael Kokkolaras
  • Sébastien Le Digabel
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Approaches Tags , , ,

    This work introduces the StoMADS-PB algorithm for constrained stochastic blackbox optimization, which is an extension of the mesh adaptive direct-search (MADS) method originally developed for deterministic blackbox optimization under general constraints. The values of the objective and constraint functions are provided by a noisy blackbox, i.e., they can only be computed with random noise whose … Read more

    Two decades of blackbox optimization applications

  • Stéphane Alarie
  • Charles Audet
  • Aïmen E. Gheribi
  • Michael Kokkolaras
  • Sébastien Le Digabel
    • Categories Applications - Science and Engineering, Chemical Engineering, Mechanical Engineering Tags , , ,

    This work reviews blackbox optimization applications over the last twenty years, addressed using direct search optimization methods. Emphasis is placed on the Mesh Adaptive Direct Search (MADS) derivative-free optimization algorithm. The core of the document describes applications in three specific fields: Energy, materials science, and computational engineering design. Other applications in science and engineering as … Read more

    A Noise-Tolerant Quasi-Newton Method for Unconstrained Optimization

  • Richard H. Byrd
  • Jorge Nocedal
  • Hao-Jun Shi
  • Yuchen Xie
    • Categories Unconstrained Optimization Tags , , ,

    This paper describes an extension of the BFGS and L-BFGS methods for the minimization of a nonlinear function subject to errors. This work is motivated by applications that contain computational noise, employ low-precision arithmetic, or are subject to statistical noise. The classical BFGS and L-BFGS methods can fail in such circumstances because the updating procedure … Read more

    Using first-order information in Direct Multisearch for multiobjective optimization

  • Roberto Andreani
  • Ana Luisa Custódio
  • Marcos Raydan
    • Categories Bound-constrained Optimization, Multi-Criteria Optimization, Nonlinear Optimization Tags , , , ,

    Derivatives are an important tool for single-objective optimization. In fact, it is commonly accepted that derivative-based methods present a better performance than derivative-free optimization approaches. In this work, we will show that the same does not apply to multiobjective derivative-based optimization, when the goal is to compute an approximation to the complete Pareto front of … Read more

    Inexact Derivative-Free Optimization for Bilevel Learning

  • Matthias J. Ehrhardt
  • Lindon Roberts
    • Categories Nonlinear Optimization Tags , , ,

    Variational regularization techniques are dominant in the field of mathematical imaging. A drawback of these techniques is that they are dependent on a number of parameters which have to be set by the user. A by now common strategy to resolve this issue is to learn these parameters from data. While mathematically appealing this strategy … Read more

    DMulti-MADS: Mesh adaptive direct multisearch for blackbox multiobjective optimization

  • Jean Bigeon
  • Sébastien Le Digabel
  • Ludovic Salomon
    • Categories Multi-Criteria Optimization, Nonlinear Optimization Tags , , , ,

    The context of this research is multiobjective optimization where conflicting objectives are present. In this work, these objectives are only available as the outputs of a blackbox for which no derivative information is available. This work proposes a new extension of the mesh adaptive direct search (MADS) algorithm to constrained multiobjective derivative-free optimization. This method … Read more

    Expected complexity analysis of stochastic direct-search

  • Kwassi Joseph Dzahini
    • Categories Nonlinear Optimization, Stochastic Approaches, Unconstrained Optimization Tags , , , , ,

    This work presents the convergence rate analysis of stochastic variants of the broad class of direct-search methods of directional type. It introduces an algorithm designed to optimize differentiable objective functions $f$ whose values can only be computed through a stochastically noisy blackbox. The proposed stochastic directional direct-search (SDDS) algorithm accepts new iterates by imposing a … Read more

    Zero Order Stochastic Weakly Convex Composite Optimization

  • Vyacheslav Kungurtsev
  • Francesco Rinaldi
    • Categories Nonlinear Optimization, Nonsmooth Optimization, Stochastic Programming Tags , , ,

    In this paper we consider stochastic weakly convex composite problems, however without the existence of a stochastic subgradient oracle. We present a derivative free algorithm that uses a two point approximation for computing a gradient estimate of the smoothed function. We prove convergence at a similar rate as state of the art methods, however with … Read more

    Exploiting problem structure in derivative free optimization

  • Margherita Porcelli
  • Philippe L. Toint
    • Categories Nonlinear Optimization, Optimization Software and Modeling Systems Tags , , ,

    A structured version of derivative-free random pattern search optimization algorithms is introduced which is able to exploit coordinate partially separable structure (typically associated with sparsity) often present in unconstrained and bound-constrained optimization problems. This technique improves performance by orders of magnitude and makes it possible to solve large problems that otherwise are totally intractable by … Read more