Stochastic Programming

On the Convergence and Complexity of Proximal Gradient and Accelerated Proximal Gradient Methods under Adaptive Gradient Estimation

  • Raghu Bollapragada
  • Shagun Gupta
    • Categories Nonlinear Optimization, Optimization in Data Science, Stochastic Programming Tags , , , ,

    In this paper, we propose a proximal gradient method and an accelerated proximal gradient method for solving composite optimization problems, where the objective function is the sum of a smooth and a convex, possibly nonsmooth, function. We consider settings where the smooth component is either a finite-sum function or an expectation of a stochastic function, … Read more

    Anesthesiologist Scheduling with Handoffs: A Combined Approach of Optimization and Human Factors

  • Ankit Bansal
  • Osman Y. Ozaltin
    • Categories Integer Programming, Scheduling, Stochastic Programming

    We present a two-stage stochastic programming model for optimizing anesthesiologist schedules, explicitly accounting for uncertainty in surgery durations and anesthesiologist handoffs. To inform model design, we conducted an online survey at our partner institution to identify key factors affecting the quality of intraoperative anesthesiologist handoffs. Insights from the survey results are incorporated into the model, … Read more

    Distributionally Robust Universal Classification: Bypassing the Curse of Dimensionality

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

    The Universal Classification (UC) problem seeks an optimal classifier from a universal policy space to minimize the expected 0-1 loss, also known as the misclassification risk. However, the conventional empirical risk minimization often leads to overfitting and poor out-of-sample performance. To address this limitation, we introduce the Distributionally Robust Universal Classification (DRUC) formulation, which incorporates … Read more

    Investment and Operational Planning for electricity markets with massive entry of renewable energy

  • Matías Vera
  • Alejandro Jofré
  • Nicolas Hernández-Santibáñez
    • Categories Dynamic Programming, Network Optimization, Stochastic Programming Tags

    In wholesale electricity markets, electricity producers and the \emph{independent system operator} (ISO) play a central role. The ISO is responsible for minimizing production costs while satisfying supply–demand balance and capacity constraints. In this paper, we study a continuous-time problem in which the ISO seeks to minimize the joint cost of operation and investment in an … Read more

    First-order methods for stochastic and finite-sum convex optimization with deterministic constraints

  • Zhaosong Lu
  • Yifeng Xiao
    • Categories Constrained Nonlinear Optimization, Convex Optimization, Stochastic Programming Tags , , , , , ,

    In this paper, we study a class of stochastic and finite-sum convex optimization problems with deterministic constraints. Existing methods typically aim to find an \(\epsilon\)-expectedly feasible stochastic optimal solution, in which the expected constraint violation and expected optimality gap are both within a prescribed tolerance ϵ. However, in many practical applications, constraints must be nearly … Read more

    Clean Electricity Transition under Technology Cost and Efficiency Uncertainty for Distributed Energy Systems: A Multi-Stage Stochastic Programming Framework

  • Ahmet Emir Şener
  • Burak Kocuk
  • Tuğçe Yüksel
    • Categories Energy, Stochastic Programming Tags , , ,

    The decarbonization of energy systems is an essential component of global climate mitigation, yet such transitions involve substantial capital requirements, ongoing technological progress, and the operational complexities of renewable integration. This study presents a dynamic strategic planning framework that applies multi-stage stochastic programming to guide clean electricity transitions for distributed energy systems. The model jointly … Read more

    Toward Decision-Oriented Prognostics: An Integrated Estimate-Optimize Framework for Predictive Maintenance

  • Zhuojun Xie
  • Adam Abdin
  • Yiping Fang
    • Categories Optimization in Data Science, Stochastic Programming Tags , , , ,

    Recent research increasingly integrates machine learning (ML) into predictive maintenance (PdM) to reduce operational and maintenance costs in data-rich operational settings. However, uncertainty due to model misspecification continues to limit widespread industrial adoption. This paper investigates a PdM framework in which sensor-driven prognostics inform decision-making under economic trade-offs within a finite decision space. We investigate … Read more

    The L-Shaped Method for Stochastic Programs with Decision-Dependent Uncertainty

  • Giovanni Pantuso
  • Mike Hewitt
    • Categories Stochastic Programming

    In this paper we extend the well-known L-Shaped method to solve two-stage stochastic programming problems with decision-dependent uncertainty. The method is based on a novel, unifying, formulation and on distribution-specific optimality and feasibility cuts for both linear and integer stochastic programs. Extensive tests on three production planning problems illustrate that the method is extremely effective … Read more

    Algorithmic Approaches for Identifying the Trade-off between Pessimism and Optimism in a Stochastic Fixed Charge Facility Location Problem

  • Man Yiu Tsang
  • Karmel S. Shehadeh
    • Categories Facility Planning and Design, Robust Optimization, Stochastic Programming Tags , , ,

    We introduce new algorithms to identify the trade-off (TRO) between adopting a distributional belief and hedging against ambiguity when modeling uncertainty in a capacitated fixed charge facility location problem (CFLP). We first formulate a TRO model for the CFLP (TRO-CFLP), which determines the number of facilities to open by minimizing the fixed establishment cost and … Read more

    ASMOP: Additional sampling stochastic trust region method for multi-objective problems

  • Nataša Krklec Jerinkić
  • Luka Rutešić
  • Ilaria Trombini
    • Categories Multi-Criteria Optimization, Optimization of Systems modeled by PDEs, Stochastic Programming Tags , , , , ,

    We consider unconstrained multi-criteria optimization problems with finite sum objective functions. The proposed algorithm belongs to a non-monotone trust region framework where additional sampling approach is used to govern the sample size and the acceptance of a candidate point. Depending on the problem, the method can yield a mini-batch or an increasing sample size behavior. … Read more