Stochastic Programming

HOGWILD!: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent

  • Feng Niu
  • Christopher Re
  • Benjamin Recht
  • Stephen Wright
    • Categories Data-Mining, Nonlinear Optimization, Stochastic Programming Tags , , , ,

    Stochastic Gradient Descent (SGD) is a popular algorithm that can achieve state-of-the-art performance on a variety of machine learning tasks. Several researchers have recently proposed schemes to parallelize SGD, but all require performance-destroying memory locking and synchronization. This work aims to show using novel theoretical analysis, algorithms, and implementation that SGD can be implemented *without … Read more

    Convex relaxations of chance constrained optimization problems

  • Shabbir Ahmed
    • Categories Stochastic Programming Tags ,

    In this paper we develop convex relaxations of chance constrained optimization problems in order to obtain lower bounds on the optimal value. Unlike existing statistical lower bounding techniques, our approach is designed to provide deterministic lower bounds. We show that a version of the proposed scheme leads to a tractable convex relaxation when the chance … Read more

    A Bilevel Direct Search Method for Leader-Follower Optimization Problems and Applications

  • Gui-Hua Lin
  • Dali Zhang
    • Categories Game Theory, Other Topics, Stochastic Programming Tags , , , ,

    In the paper, we propose a bilevel direct search method for solving a type of leader-follower problems with each decision maker’s objective being a “black-box” function. First, we give a description for a leader-follower optimization problem. Then, we investigate a bilevel direct search method including two algorithms for combinatorially solving the upper and lower level … Read more

    Distributionally robust workforce scheduling in call centers with uncertain arrival rates

  • Shuangqing Liao
  • Jean-Philippe Vial
  • Christian van Delft
    • Categories Robust Optimization, Stochastic Programming

    Call center scheduling aims to set-up the workforce so as to meet target service levels. The service level depends on the mean rate of arrival calls, which fluctuates during the day and from day to day. The staff scheduling must adjust the workforce period per period during the day, but the flexibility in so doing … Read more

    A Branch-and-Cut Decomposition Algorithm for Solving Chance-Constrained Mathematical Programs with Finite Support

  • James R. Luedtke
    • Categories Stochastic Programming Tags , , , ,

    We present a new approach for exactly solving chance-constrained mathematical programs having discrete distributions with nite support and random polyhedral constraints. Such problems have been notoriously difficult to solve due to nonconvexity of the feasible region, and most available methods are only able to nd provably good solutions in certain very special cases. Our approach … Read more

    Stochastic programs without duality gaps

  • Teemu Pennanen
  • Ari-Pekka Perkkiö
    • Categories Stochastic Programming Tags , ,

    This paper studies dynamic stochastic optimization problems parametrized by a random variable. Such problems arise in many applications in operations research and mathematical finance. We give sufficient conditions for the existence of solutions and the absence of a duality gap. Our proof uses extended dynamic programming equations, whose validity is established under new relaxed conditions … Read more

    Level methods uniformly optimal for composite and structured nonsmooth convex optimization

  • Guanghui Lan
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming, Stochastic Programming Tags , , , , ,

    The main goal of this paper is to develop uniformly optimal first-order methods for large-scale convex programming (CP). By uniform optimality we mean that the first-order methods themselves do not require the input of any problem parameters, but can still achieve the best possible iteration complexity bounds. To this end, we provide a substantial generalization … Read more

    Level methods uniformly optimal for composite and structured nonsmooth convex optimization

  • Guanghui Lan
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming, Stochastic Programming Tags , , , , ,

    The main goal of this paper is to develop uniformly optimal first-order methods for large-scale convex programming (CP). By uniform optimality we mean that the first-order methods themselves do not require the input of any problem parameters, but can still achieve the best possible iteration complexity bounds. To this end, we provide a substantial generalization … Read more

    Scalable Stochastic Optimization of Complex Energy Systems

  • Mihai Anitescu
  • Victor M. Zavala
  • Miles Lubin
  • Cosmin G. Petra
    • Categories Parallel Algorithms, Stochastic Programming Tags , , , ,

    We present a scalable approach and implementation for solving stochastic programming problems, with application to the optimization of complex energy systems under uncertainty. Stochastic programming is used to make decisions in the present while incorporating a model of uncertainty about future events (scenarios). These problems present serious computational difficulties as the number of scenarios becomes … Read more

    A Matrix-Free Approach For Solving The Gaussian Process Maximum Likelihood Problem

  • Mihai Anitescu
    • Categories Nonlinear Optimization, Statistics, Stochastic Programming Tags , , , , , ,

    Gaussian processes are the cornerstone of statistical analysis in many application ar- eas. Nevertheless, most of the applications are limited by their need to use the Cholesky factorization in the computation of the likelihood. In this work, we present a matrix-free approach for comput- ing the solution of the maximum likelihood problem involving Gaussian processes. … Read more