Stochastic Programming

Managing Distributional Ambiguity in Stochastic Optimization through a Statistical Upper Bound Framework

  • Shixin Liu
  • Jian Hu
    • Categories Robust Optimization, Stochastic Programming

    Stochastic optimization is often hampered by distributional ambiguity, where critical probability distributions are poorly characterized or unknown. Addressing this challenge, we introduce a new framework that targets the minimization of a statistical upper bound for the expected value of uncertain objectives, facilitating more statistically robust decision-making. Central to our approach is the Average Percentile Upper … Read more

    Uncertainty Quantification for Multiobjective Stochastic Convex Quadratic Programs

  • Ziyu Liu
  • Susan R. Hunter
  • Nan Kong
  • Raghu Pasupathy
    • Categories Multi-Criteria Optimization, Quadratic Programming, Stochastic Programming

    A multiobjective stochastic convex quadratic program (MOSCQP) is a multiobjective optimization problem with convex quadratic objectives that are observed with stochastic error. MOSCQP is a useful problem formulation arising, for example, in model calibration and nonlinear system identification when a single regression model combines data from multiple distinct sources, resulting in a multiobjective least squares … Read more

    Problem-Parameter-Free Decentralized Nonconvex Stochastic Optimization

  • Jiaxiang Li
  • Xuxing Chen
  • Shiqian Ma
  • Mingyi Hong
    • Categories Nonlinear Optimization, Stochastic Programming

    Existing decentralized algorithms usually require knowledge of problem parameters for updating local iterates. For example, the hyperparameters (such as learning rate) usually require the knowledge of Lipschitz constant of the global gradient or topological information of the communication networks, which are usually not accessible in practice. In this paper, we propose D-NASA, the first algorithm … Read more

    Variance Reduction and Low Sample Complexity in Stochastic Optimization via Proximal Point Method

  • Jiaming Liang
    • Categories Convex Optimization, Stochastic Programming Tags , , , , ,

    This paper proposes a stochastic proximal point method to solve a stochastic convex composite optimization problem. High probability results in stochastic optimization typically hinge on restrictive assumptions on the stochastic gradient noise, for example, sub-Gaussian distributions. Assuming only weak conditions such as bounded variance of the stochastic gradient, this paper establishes a low sample complexity … Read more

    Extending the Reach of First-Order Algorithms for Nonconvex Min-Max Problems with Cohypomonotonicity

  • Ahmet Alacaoglu
  • Donghwan Kim
  • Stephen Wright
    • Categories Complementarity and Variational Inequalities, Generalized Convexity/Monoticity, Nonlinear Optimization, Stochastic Programming

    We focus on constrained, \(L\)-smooth, nonconvex-nonconcave min-max problems either satisfying \(\rho\)-cohypomonotonicity or admitting a solution to the \(\rho\)-weakly Minty Variational Inequality (MVI), where larger values of the parameter \(\rho>0\) correspond to a greater degree of nonconvexity. These problem classes include examples in two player reinforcement learning, interaction dominant min-max problems, and certain synthetic test problems on … Read more

    Distributionally Fair Stochastic Optimization using Wasserstein Distance

  • Ye Qing
  • Grani A. Hanasusanto
  • Weijun Xie
    • Categories Stochastic Programming Tags , ,

    A traditional stochastic program under a finite population typically seeks to optimize efficiency by maximizing the expected profits or minimizing the expected costs, subject to a set of constraints. However, implementing such optimization-based decisions can have varying impacts on individuals, and when assessed using the individuals’ utility functions, these impacts may differ substantially across demographic … Read more

    On Tractability, Complexity, and Mixed-Integer Convex Programming Representability of Distributionally Favorable Optimization

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

    Distributionally Favorable Optimization (DFO) is an important framework for decision-making under uncertainty, with applications across fields such as reinforcement learning, online learning, robust statistics, chance-constrained programming, and two-stage stochastic optimization without relatively complete recourse. In contrast to the traditional Distributionally Robust Optimization (DRO) paradigm, DFO presents a unique challenge– the application of the inner infimum … Read more

    Policy with guaranteed risk-adjusted performance for multistage stochastic linear problems

  • Bernardo Freitas Paulo da Costa
  • Vincent Leclère
    • Categories Convex Optimization, Dynamic Programming, Stochastic Programming Tags , , ,

    Risk-averse multi-stage problems and their applications are gaining interest in various fields of applications. Under convexity assumptions, the resolution of these problems can be done with trajectory following dynamic programming algorithms like Stochastic Dual Dynamic Programming (SDDP) to access a deterministic lower bound, and dual SDDP for deterministic upper bounds. In this paper, we leverage … Read more

    Active Set-based Inexact Proximal Bundle Algorithm for Stochastic Quadratic Programming

  • Niloofar Fadavi
  • Harsha Gangammanavar
    • Categories Convex Optimization, Stochastic Programming Tags , , , ,

    In this paper, we examine two-stage stochastic quadratic programming problems, where the objective function of the first and second stages are quadratic functions, and the constraints are linear. The uncertainty is associated with the second-stage right-hand side and variable bounds. In large-scale settings, when the number of scenarios necessary to represent the underlying stochastic process … Read more

    Design Guidelines for Noise-Tolerant Optimization with Applications in Robust Design

  • Yuchen Lou
  • Shigeng Sun
  • Jorge Nocedal
    • Categories Nonlinear Optimization, Optimization of Systems modeled by PDEs, Stochastic Programming

    The development of nonlinear optimization algorithms capable of performing reliably in the presence of noise has garnered considerable attention lately. This paper advocates for strategies to create noise-tolerant nonlinear optimization algorithms by adapting classical deterministic methods. These adaptations follow certain design guidelines described here, which make use of estimates of the noise level in the … Read more