Stochastic Programming

Zeroth-Order Methods for Nonconvex-Strongly Concave Stochastic Minimax Problems with Decision-Dependent Distributions

  • Zililuo
  • Yongchao Liu
    • Categories Nonlinear Optimization, Optimization in Data Science, Stochastic Programming Tags , , , ,

    Stochastic minimax problems with decision-dependent distributions (SMDD) have emerged as a crucial framework for modeling complex systems where data distributions drift in response to decision variables. Most existing methods for SMDD rely on an explicit functional relationship between the decision variables and the probability distribution. In this paper, we propose two sample-based zeroth-order algorithms, namely … Read more

    Data-driven Policies For Two-stage Stochastic Linear Programs

  • chhavisharma
  • Harsha Gangammanavar
    • Categories Linear Programming, Optimization in Data Science, Stochastic Programming Tags , , ,

    A stochastic program typically involves several parameters, including deterministic first-stage parameters and stochastic second-stage elements that serve as input data. These programs are re-solved whenever any input parameter changes. However, in practical applications, quick decision-making is necessary, and solving a stochastic program from scratch for every change in input data can be computationally costly. This … Read more

    Solving Chance Constrained Programs via a Penalty based Difference of Convex Approach

  • ZhipingLi
  • Nan Jiang
  • Rujun Jiang
    • Categories Nonlinear Optimization, Stochastic Programming Tags , , ,

    We develop two penalty based difference of convex (DC) algorithms for solving chance constrained programs. First, leveraging a rank-based DC decomposition of the chance constraint, we propose a proximal penalty based DC algorithm in the primal space that does not require a feasible initialization. Second, to improve numerical stability in the general nonlinear settings, we … Read more

    Chance-Constrained Linear Complementarity Problems

  • René Henrion
  • Martin Schmidt
    • Categories Complementarity and Variational Inequalities, Energy, Stochastic Programming Tags , , , , ,

    We study linear complementarity problems (LCPs) under uncertainty, which we model using chance constraints. Since the complementarity condition of the LCP is an equality constraint, it is required to consider relaxations, which naturally leads to optimization problems in which the relaxation parameters are minimized for given probability levels. We focus on these optimization problems and … Read more

    Normalization of ReLU Dual for Cut Generation in Stochastic Mixed-Integer Programs

  • Akul Bansal
  • Simge Küçükyavuz
    • Categories Stochastic Programming Tags , ,

    We study the Rectified Linear Unit (ReLU) dual, an existing dual formulation for stochastic programs that reformulates non-anticipativity constraints using ReLU functions to generate tight, non-convex, and mixed-integer representable cuts. While this dual reformulation guarantees convergence with mixed-integer state variables, it admits multiple optimal solutions that can yield weak cuts. To address this issue, we … Read more

    Projected Stochastic Momentum Methods for Nonlinear Equality-Constrained Optimization for Machine Learning

  • Qi Wang
  • Yunlang Zhu
  • Frank E. Curtis
  • Christian Piermarini
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Programming Tags , , , , ,

    Two algorithms are proposed, analyzed, and tested for solving continuous optimization problems with nonlinear equality constraints. Each is an extension of a stochastic momentum-based method from the unconstrained setting to the setting of a stochastic Newton-SQP-type algorithm for solving equality-constrained problems. One is an extension of the heavy-ball method and the other is an extension … Read more

    Contextual Distributionally Robust Optimization with Causal and Continuous Structure: An Interpretable and Tractable Approach

  • Jie Wang
    • Categories Optimization in Data Science, Robust Optimization, Stochastic Programming Tags ,

    In this paper, we introduce a framework for contextual distributionally robust optimization (DRO) that considers the causal and continuous structure of the underlying distribution by developing interpretable and tractable decision rules that prescribe decisions using covariates. We first introduce the causal Sinkhorn discrepancy (CSD), an entropy-regularized causal Wasserstein distance that encourages continuous transport plans while … Read more

    First-order Methods for Unconstrained Vector Optimization Problems: A Unified Majorization-Minimization Perspective

  • Jian Chen
    • Categories Multi-Criteria Optimization, Nonlinear Optimization, Stochastic Programming Tags , , , ,

    In this paper, we develop a unified majorization-minimization scheme and convergence analysis with first-order surrogate functions for unconstrained vector optimization problems (VOPs). By selecting different surrogate functions, the unified method can be reduced to various existing first-order methods. The unified convergence analysis reveals that the slow convergence of the steepest descent method is primarily attributed … Read more

    A Gauge Set Framework for Flexible Robustness Design

  • Ningji Wei
  • Xian Yu
  • Peter Yun Zhang
    • Categories Robust Optimization, Stochastic Programming

    This paper proposes a unified framework for designing robustness in optimization under uncertainty using gauge sets, convex sets that generalize distance and capture how distributions may deviate from a nominal reference. Representing robustness through a gauge set reweighting formulation brings many classical robustness paradigms under a single convex-analytic perspective. The corresponding dual problem, the upper … Read more