Stochastic Programming

Subgradient Convergence Implies Subdifferential Convergence on Weakly Convex Functions: With Uniform Rates Guarantees

  • Feng Ruan
    • Categories Nonsmooth Optimization, Stochastic Programming

    In nonsmooth, nonconvex stochastic optimization, understanding the uniform convergence of subdifferential mappings is crucial for analyzing stationary points of sample average approximations of risk as they approach the population risk. Yet, characterizing this convergence remains a fundamental challenge. This work introduces a novel perspective by connecting the uniform convergence of subdifferential mappings to that of subgradient … Read more

    Multistage Stochastic Facility Location under Facility Disruption Uncertainty

  • Bonn Kleiford Seranilla
  • Nils Löhndorf
    • Categories Production and Logistics, Stochastic Programming, Supply Chain Management Tags , , ,

    We consider a multistage variant of the classical stochastic capacitated facility location problem under facility disruption uncertainty. Two solution algorithms for this problem class are presented: (1) stochastic dual dynamic integer programming (SDDiP), the state-of-the-art algorithm for solving multistage stochastic integer programs, and (2) shadow price approximation (SPA), an algorithm utilizing trained parameters of the … Read more

    A computational study of cutting-plane methods for multi-stage stochastic integer programs

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

    We report a computational study of cutting plane algorithms for multi-stage stochastic mixed-integer programming models with the following cuts: (i) Benders’, (ii) Integer L-shaped, and (iii) Lagrangian cuts. We first show that Integer L-shaped cuts correspond to one of the optimal solutions of the Lagrangian dual problem, and, therefore, belong to the class of Lagrangian … Read more

    Applying random coordinate descent in a probability maximization scheme

  • Edit Csizmás
  • Rajmund Drenyovszki
  • Tamas Szantai
  • Csaba I. Fábián
    • Categories Stochastic Programming Tags , ,

    Gradient computation of multivariate distribution functions calls for considerable effort. A standard procedure is component-wise computation, hence coordinate descent is an attractive choice. This paper deals with constrained convex problems. We apply random coordinate descent in an approximation scheme that is an inexact cutting-plane method from a dual viewpoint. We present convergence proofs and a … Read more

    Faster Convergence of Stochastic Accelerated Gradient Descent under Interpolation

  • Aaron Mishkin
  • Mert Pilanci
  • Mark Schmidt
    • Categories Convex Optimization, Stochastic Programming Tags , , ,

    We prove new convergence rates for a generalized version of stochastic Nesterov acceleration under interpolation conditions. Unlike previous analyses, our approach accelerates any stochastic gradient method which makes sufficient progress in expectation. The proof, which proceeds using the estimating sequences framework, applies to both convex and strongly convex functions and is easily specialized to accelerated … Read more

    Similarity-based Decomposition Algorithm for Two-stage Stochastic Scheduling

  • Daniel Montes
  • José Luis Pitarch
  • Cesar de Prada
    • Categories Parallel Algorithms, Scheduling, Stochastic Programming Tags , ,

    This paper presents a novel decomposition method for two-stage stochastic mixed-integer optimization problems. The algorithm builds upon the idea of similarity between finite sample sets to measure how similar the first-stage decisions are among the uncertainty realization scenarios. Using such a Similarity Index, the non-anticipative constraints are removed from the problem formulation so that the … Read more

    On the Out-of-Sample Performance of Stochastic Dynamic Programming and Model Predictive Control

  • Dominic S. T. Keehan
  • Andrew B. Philpott
  • Edward Anderson
    • Categories Dynamic Programming, Stochastic Programming Tags , , ,

    Sample average approximation–based stochastic dynamic programming (SDP) and model predictive control (MPC) are two different methods for approaching multistage stochastic optimization. In this paper we investigate the conditions under which SDP may be outperformed by MPC. We show that, depending on the presence of concavity or convexity, MPC can be interpreted as solving a mean-constrained … Read more

    A Unified Approach for Maximizing Continuous $\gamma$-weakly DR-submodular Functions

  • Mohammad Pedramfar
  • Christopher Quinn
  • Vaneet Aggarwal
    • Categories Constrained Nonlinear Optimization, Stochastic Programming Tags , ,

    This paper presents a unified approach for maximizing continuous \(\gamma\)-weakly DR-submodular functions that encompasses a range of settings and oracle access types. Our approach includes a Frank-Wolfe type offline algorithm for both monotone and non-monotone functions, with different restrictions on the convex feasible region. We consider settings where the oracle provides access to either the … Read more

    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