distributionally robust optimization

Data-Driven Optimization with Distributionally Robust Second-Order Stochastic Dominance Constraints

  • Chun Peng
  • Erick Delage
    • Categories Robust Optimization Tags , ,

    Optimization with stochastic dominance constraints has recently received an increasing amount of attention in the quantitative risk management literature. Instead of requiring that the probabilistic description of the uncertain parameters be exactly known, this paper presents the first comprehensive study of a data-driven formulation of the distributionally robust second-order stochastic dominance constrained problem (DRSSDCP) that … Read more

    Kernel Distributionally Robust Optimization

  • Moritz Diehl
  • Wittawat Jitkrittum
  • Bernhard Schölkopf
  • Jia-Jie Zhu
    • Categories Robust Optimization, Statistics, Stochastic Programming Tags , , ,

    We propose kernel distributionally robust optimization (Kernel DRO) using insights from the robust optimization theory and functional analysis. Our method uses reproducing kernel Hilbert spaces (RKHS) to construct a wide range of convex ambiguity sets, including sets based on integral probability metrics and finite-order moment bounds. This perspective unifies multiple existing robust and stochastic optimization … Read more

    Residuals-based distributionally robust optimization with covariate information

  • Rohit Kannan
  • Güzin Bayraksan
  • James R. Luedtke
    • Categories Robust Optimization, Stochastic Programming Tags , , , , , , ,

    We consider data-driven approaches that integrate a machine learning prediction model within distributionally robust optimization (DRO) given limited joint observations of uncertain parameters and covariates. Our framework is flexible in the sense that it can accommodate a variety of regression setups and DRO ambiguity sets. We investigate asymptotic and finite sample properties of solutions obtained … Read more

    A Distributionally-Robust Service Center Location Problem with Decision Dependent Demand Induced from a Maximum Attraction Principle

  • Fengqiao Luo
    • Categories Applications - OR and Management Sciences, Combinatorial Optimization, Integer Programming Tags , , , ,

    We establish and analyze a service center location model with a simple but novel decision-dependent demand induced from a maximum attrac- tion principle. The model formulations are investigated in the distributionally- robust optimization framework. A statistical model that is based on the max- imum attraction principle for estimating customer demand and utility gain from service … Read more

    A Novel Solution Methodology for Wasserstein-based Data-Driven Distributionally Robust Problems

  • Carlos Gamboa
  • Tito Homem-de-Mello
  • Alexandre Street
  • Davi Valladão
    • Categories Stochastic Programming Tags , ,

    Distributionally robust optimization (DRO) is a mathematical framework to incorporate ambiguity over the actual data-generating probability distribution. Data-driven DRO problems based on the Wasserstein distance are of particular interest for their sound mathematical properties. For right-hand-sided uncertainty, however, existing methods rely on dual vertex enumeration rendering the problem intractable in practical applications. In this context, … Read more

    Distributionally Robust Two-Stage Stochastic Programming

  • Daniel Duque
  • Sanjay Mehrotra
  • David P. Morton
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    Distributionally robust optimization is a popular modeling paradigm in which the underlying distribution of the random parameters in a stochastic optimization model is unknown. Therefore, hedging against a range of distributions, properly characterized in an ambiguity set, is of interest. We study two-stage stochastic programs with linear recourse in the context of distributional ambiguity, and … Read more

    Distributionally Robust Facility Location with Bimodal Random Demand

  • Karmel S. Shehadeh
  • Ece Sanci
    • Categories (Mixed) Integer Linear Programming, Facility Planning and Design, Stochastic Programming Tags , , , ,

    In this paper, we consider a decision-maker who wants to determine a subset of locations from a given set of candidate sites to open facilities and accordingly assign customer demand to these open facilities. Unlike classical facility location settings, we focus on a new setting where customer demand is bimodal, i.e., display, or belong to, … Read more

    Finite-Sample Guarantees for Wasserstein Distributionally Robust Optimization: Breaking the Curse of Dimensionality

  • Rui Gao
    • Categories Robust Optimization, Statistics, Stochastic Programming Tags , , , ,

    Wasserstein distributionally robust optimization (DRO) aims to find robust and generalizable solutions by hedging against data perturbations in Wasserstein distance. Despite its recent empirical success in operations research and machine learning, existing performance guarantees for generic loss functions are either overly conservative due to the curse of dimensionality, or plausible only in large sample asymptotics. … Read more

    Strong Formulations for Distributionally Robust Chance-Constrained Programs with Left-Hand Side Uncertainty under Wasserstein Ambiguity

  • Nam Ho-Nguyen
  • Fatma Kılınç-Karzan
  • Simge Küçükyavuz
  • Dabeen Lee
    • Categories Robust Optimization, Stochastic Programming Tags , , , ,

    Distributionally robust chance-constrained programs (DR-CCP) over Wasserstein ambiguity sets exhibit attractive out-of-sample performance and admit big-$M$-based mixed-integer programming (MIP) reformulations with conic constraints. However, the resulting formulations often suffer from scalability issues as sample size increases. To address this shortcoming, we derive stronger formulations that scale well with respect to the sample size. Our focus … Read more

    Equivalent second-order cone programs for distributionally robust zero-sum games

  • Ayush Agarwal
  • Abdel Lisser
  • Vikas Vikram Singh
  • Navnit Yadav
    • Categories Game Theory, Stochastic Programming Tags , , , ,

    We consider a two player zero-sum game with stochastic linear constraints. The probability distributions of the vectors associated with the constraints are partially known. The available information with respect to the distribution is based mainly on the two first moments. In this vein, we formulate the stochastic linear constraints as distributionally robust chance constraints. We … Read more