sample average approximation

Stochastic Decomposition for Two-stage Stochastic Linear Programs with Random Cost Coefficients

  • Harsha Gangammanavar
  • Yifan Liu
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , , , ,

    Stochastic decomposition (SD) has been a computationally effective approach to solve large-scale stochastic programming (SP) problems arising in practical applications. By using incremental sampling, this approach is designed to discover an appropriate sample size for a given SP instance, thus precluding the need for either scenario reduction or arbitrary sample sizes to create sample average … Read more

    Shortfall Risk Models When Information of Loss Function Is Incomplete

  • Erick Delage
  • Shaoyan Guo
  • Huifu Xu
    • Categories Linear Programming, Robust Optimization, Stochastic Programming Tags , , , , , ,

    Utility-based shortfall risk measure (SR) has received increasing attentions over the past few years for its potential to quantify more effectively the risk of large losses than conditional value at risk. In this paper we consider the case that the true loss function is unavailable either because it is difficult to be identified or the … Read more

    Convergence Analysis of Sample Average Approximation of Two-stage Stochastic Generalized Equations

  • Xiaojun Chen
  • Alexander Shapiro
  • Hailin Sun
    • Categories Stochastic Programming Tags , , , ,

    A solution of two-stage stochastic generalized equations is a pair: a first stage solution which is independent of realization of the random data and a second stage solution which is a function of random variables. This paper studies convergence of the sample average approximation of two-stage stochastic nonlinear generalized equations. In particular an exponential rate … Read more

    Sample Average Approximation with Adaptive Importance Sampling

  • Mingbin Feng
  • Alvaro Maggiar
  • Jeremy Staum
  • Andreas Wächter
    • Categories Stochastic Programming Tags , , , ,

    We study sample average approximations under adaptive importance sampling in which the sample densities may depend on previous random samples. Based on a generic uniform law of large numbers, we establish uniform convergence of the sample average approximation to the true function. We obtain convergence of the optimal value and optimal solutions of the sample … Read more

    A Stochastic Programming Approach for Electric Vehicle Charging Network Design

  • Ratna Chinnam
  • Sina Faridimehr
  • Saravanan Venkatachalam
    • Categories Stochastic Programming, Transportation Tags , , ,

    Advantages of electric vehicles (EV) include reduction of greenhouse gas and other emissions, energy security, and fuel economy. The societal benefits of large-scale adoption of EVs cannot be realized without adequate deployment of publicly accessible charging stations. We propose a two-stage stochastic programming model to determine the optimal network of charging stations for a community … Read more

    Statistical inference and hypotheses testing of risk averse stochastic programs

  • Vincent Guigues
  • Alexander Shapiro
  • Volker Krätschmer
    • Categories Stochastic Programming Tags , , , , ,

    We study statistical properties of the optimal value and optimal solutions of the Sample Average Approximation of risk averse stochastic problems. Central Limit Theorem type results are derived for the optimal value when the stochastic program is expressed in terms of a law invariant coherent risk measure having a discrete Kusuoka representation. The obtained results … Read more

    A Sequential Algorithm for Solving Nonlinear Optimization Problems with Chance Constraints

  • Frank E. Curtis
  • Victor M. Zavala
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization Tags , , , , , ,

    An algorithm is presented for solving nonlinear optimization problems with chance constraints, i.e., those in which a constraint involving an uncertain parameter must be satisfied with at least a minimum probability. In particular, the algorithm is designed to solve cardinality-constrained nonlinear optimization problems that arise in sample average approximations of chance-constrained problems, as well as … Read more

    Statistical inference and hypotheses testing of risk averse stochastic programs

  • Vincent Guigues
  • Alexander Shapiro
  • Volker Krätschmer
    • Categories Statistics, Stochastic Programming Tags , , , , ,

    We study statistical properties of the optimal value and optimal solutions of the Sample Average Approximation of risk averse stochastic problems. Central Limit Theorem type results are derived for the optimal value and optimal solutions when the stochastic program is expressed in terms of a law invariant coherent risk measure. The obtained results are applied … Read more

    An empirical analysis of scenario generation methods for stochastic optimization

  • Nils Löhndorf
    • Categories Stochastic Programming Tags , , , , , , ,

    This work presents an empirical analysis of popular scenario generation methods for stochastic optimization, including quasi-Monte Carlo, moment matching, and methods based on probability metrics, as well as a new method referred to as Voronoi cell sampling. Solution quality is assessed by measuring the error that arises from using scenarios to solve a multi-dimensional newsvendor … Read more

    Distributionally Robust Stochastic Programming

  • Alexander Shapiro
    • Categories Robust Optimization, Stochastic Programming Tags , , , , ,

    In this paper we study distributionally robust stochastic programming in a setting where there is a specified reference probability measure and the uncertainty set of probability measures consists of measures in some sense close to the reference measure. We discuss law invariance of the associated worst case functional and consider two basic constructions of such … Read more