Stochastic Programming

Tractable Reformulations of Distributionally Robust Two-stage Stochastic Programs with $\infty- Distance

  • Weijun Xie
    • Categories Stochastic Programming Tags , , , ,

    This paper studies a two-stage distributionally robust stochastic linear program under the type-∞ Wasserstein ball by providing sufficient conditions under which the program can be efficiently computed via a tractable convex program. By exploring the properties of binary variables, the developed reformulation techniques are extended to those with mixed binary random parameters. The main tractable … Read more

    Gaining traction – On the convergence of an inner approximation scheme for probability maximization

  • Csaba I. Fábián
    • Categories Convex Optimization, Stochastic Programming Tags ,

    We analyze an inner approximation scheme for probability maximization. The approach was proposed in Fabian, Csizmas, Drenyovszki, Van Ackooij, Vajnai, Kovacs, Szantai (2018) Probability maximization by inner approximation, Acta Polytechnica Hungarica 15:105-125, as an analogue of a classic dual approach in the handling of probabilistic constraints. Even a basic implementation of the maximization scheme proved … Read more

    Distributionally Robust Optimization: A Review

  • Hamed Rahimian
  • Sanjay Mehrotra
    • Categories Robust Optimization, Stochastic Programming

    The concepts of risk-aversion, chance-constrained optimization, and robust optimization have developed significantly over the last decade. Statistical learning community has also witnessed a rapid theoretical and applied growth by relying on these concepts. A modeling framework, called distributionally robust optimization (DRO), has recently received significant attention in both the operations research and statistical learning communities. … Read more

    A Data Efficient and Feasible Level Set Method for Stochastic Convex Optimization with Expectation Constraints

  • Qihang Lin
  • Selvaprabu Nadarajah
  • Negar Soheili
  • Tianbao Yang
    • Categories Convex Optimization, Stochastic Programming Tags , , ,

    Stochastic convex optimization problems with expectation constraints (SOECs) are encountered in statistics and machine learning, business, and engineering. In data-rich environments, the SOEC objective and constraints contain expectations defined with respect to large datasets. Therefore, efficient algorithms for solving such SOECs need to limit the fraction of data points that they use, which we refer … Read more

    Robust stochastic optimization with the proximal point method

  • Damek Davis
  • Dmitriy Drusvyatskiy
    • Categories Convex Optimization, Stochastic Programming Tags , , , ,

    Standard results in stochastic convex optimization bound the number of samples that an algorithm needs to generate a point with small function value in expectation. In this work, we show that a wide class of such algorithms on strongly convex problems can be augmented with sub-exponential confidence bounds at an overhead cost that is only … Read more

    Logic-based Benders Decomposition and Binary Decision Diagram Based Approaches for Stochastic Distributed Operating Room Scheduling

  • Dionne M. Aleman
  • Merve Bodur
  • Cheng Guo
  • David R. Urbach
    • Categories Cutting Plane Approaches, Scheduling, Stochastic Programming

    The distributed operating room (OR) scheduling problem aims to find an assignment of surgeries to ORs across collaborating hospitals that share their waiting lists and ORs. We propose a stochastic extension of this problem where surgery durations are considered to be uncertain. In order to obtain solutions for the challenging stochastic model, we use sample … Read more

    A conservative convergent solution for continuously distributed two-stage stochastic optimization problems

  • Carlos Gamboa
  • Alexandre Street
  • Davi Valladão
    • Categories Stochastic Programming Tags , ,

    Two-stage stochastic programming is a mathematical framework widely used in real- life applications such as power system operation planning, supply chains, logistics, inventory management, and financial planning. Since most of these problems cannot be solved analytically, decision makers make use of numerical methods to obtain a near-optimal solution. Some applica- tions rely on the implementation … Read more

    Mixed Integer Programming models for planning maintenance at offshore wind farms under uncertainty

  • Albert H. Schrotenboer
  • Evrim Ursavas
  • Iris F. A. Vis
    • Categories (Mixed) Integer Linear Programming, Stochastic Programming, Transportation Tags , , , , ,

    We introduce the Stochastic Maintenance Fleet Transportation Problem for Offshore wind farms (SMFTPO), in which a maintenance provider determines an optimal, medium-term planning for maintaining multiple wind farms while controlling for uncertainty in the maintenance tasks and weather conditions. Since the maintenance provider is typically not the owner of a wind farm, it needs to … Read more

    Distributionally robust chance constrained geometric optimization

  • Jia Liu
  • Abdel Lisser
  • Zhiping Chen
    • Categories Convex Optimization, Robust Optimization, Stochastic Programming Tags , , ,

    This paper discusses distributionally robust geometric programs with individual and joint chance constraints. Seven groups of uncertainty sets are considered: uncertainty sets with first two order moments information, uncertainty sets constrained by the Kullback-Leibler divergence distance with a normal reference distribution or a discrete reference distribution, uncertainty sets with known first moments or known first … Read more

    Tight tail probability bounds for distribution-free decision making

  • Ruud C.M. Brekelmans
  • Dick den Hertog
  • Wouter Van Eekelen
  • Ernst Roos
  • Johan Van Leeuwaarden
    • Categories Robust Optimization, Stochastic Programming Tags , , ,

    Chebyshev’s inequality provides an upper bound on the tail probability of a random variable based on its mean and variance. While tight, the inequality has been criticized for only being attained by pathological distributions that abuse the unboundedness of the underlying support and are not considered realistic in many applications. We provide alternative tight lower … Read more