Stochastic Programming

Hardness of pricing routes for two-stage stochastic vehicle routing problems with scenarios

  • Matheus Jun Ota
  • Ricardo Fukasawa
    • Categories Combinatorial Optimization, Integer Programming, Stochastic Programming Tags , ,

    The vehicle routing problem with stochastic demands (VRPSD) generalizes the classic vehicle routing problem by considering customer demands as random variables. Similarly to other vehicle routing variants, state-of-the-art algorithms for the VRPSD are often based on set-partitioning formulations, which require efficient routines for the associated pricing problems. However, all these set-partitioning-based approaches have strong assumptions … Read more

    An optimally fast objective-function-free minimization algorithm using random subspaces

  • Stefania Bellavia
  • Serge Gratton
  • Benedetta Morini
  • Philippe L. Toint
    • Categories Nonlinear Optimization, Stochastic Programming, Unconstrained Optimization Tags , , , ,

    Article Download View An optimally fast objective-function-free minimization algorithm using random subspaces

    Zeroth-order Riemannian Averaging Stochastic Approximation Algorithms

  • Jiaxiang Li
  • Krishnakumar Balasubramanian
  • Shiqian Ma
    • Categories Constrained Nonlinear Optimization, Nonlinear Optimization, Stochastic Programming

    We present Zeroth-order Riemannian Averaging Stochastic Approximation (\texttt{Zo-RASA}) algorithms for stochastic optimization on Riemannian manifolds. We show that \texttt{Zo-RASA} achieves optimal sample complexities for generating $\epsilon$-approximation first-order stationary solutions using only one-sample or constant-order batches in each iteration. Our approach employs Riemannian moving-average stochastic gradient estimators, and a novel Riemannian-Lyapunov analysis technique for convergence analysis. … Read more

    A Bilevel Optimization Approach for a Class of Combinatorial Problems with Disruptions and Probing

  • Leonardo Lozano
  • Juan Borrero
    • Categories Applications - OR and Management Sciences, Network Optimization, Stochastic Programming Tags , , ,

    \(\) We consider linear combinatorial optimization problems under uncertain disruptions that increase the cost coefficients of the objective function. A decision-maker, or planner, can invest resources to probe the components (i.e., the coefficients) in order to learn their disruption status. In the proposed probing optimization problem, the planner, knowing just the disruptions’ probabilities, selects which … Read more

    Inexact Newton methods with matrix approximation by sampling for nonlinear least-squares and systems

  • Stefania Bellavia
  • Greta Malaspina
  • Benedetta Morini
    • Categories Nonlinear Optimization, Nonlinear Systems and Least-Squares, Stochastic Programming

    We develop and analyze stochastic inexact Gauss-Newton methods for nonlinear least-squares problems and inexact Newton methods for nonlinear systems of equations. Random models are formed using suitable sampling strategies for the matrices involved in the deterministic models. The analysis of the expected number of iterations needed in the worst case to achieve a desired level … Read more

    The Terminator: An Integration of Inner and Outer Approximations for Solving Regular and Distributionally Robust Chance Constrained Programs via Variable Fixing

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

    We present a novel approach aimed at enhancing the efficacy of solving both regular and distributionally robust chance constrained programs using an empirical reference distribution. In general, these programs can be reformulated as mixed-integer programs (MIPs) by introducing binary variables for each scenario, indicating whether a scenario should be satisfied. While existing methods have predominantly … Read more

    Gradient-based rho Parameter for Progressive Hedging

  • Ulysse Naepels
  • David L. Woodruff
    • Categories Stochastic Programming Tags , , ,

    \(\) Watson and Woodruff  (2011) developed a heuristic for computing variable-dependent values of the penalty parameter $\rho$ from the model itself. We combine this heuristic with a gradient-based method, in order to obtain a new method for calculating $\rho$ values. We then introduce a method for iteratively computing variable-dependent $\rho$ values. This method is based … Read more

    Distributions and Bootstrap for Data-based Stochastic Programming

  • Xiaotie Chen
  • David L. Woodruff
    • Categories Stochastic Programming Tags , , ,

    In the context of optimization under uncertainty, we consider various combinations of distribution estimation and resampling (bootstrap and bagging) for obtaining samples used to acquire a solution and for computing a confidence interval for an optimality gap. This paper makes three experimental contributions to on-going research in data driven stochastic programming: a) most of the … Read more

    Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization

  • Frank E. Curtis
  • Xin Jiang
  • Qi Wang
    • Categories Constrained Nonlinear Optimization, Data Science Applications, Stochastic Programming Tags , , ,

    Stochastic sequential quadratic optimization (SQP) methods for solving continuous optimization problems with nonlinear equality constraints have attracted attention recently, such as for solving large-scale data-fitting problems subject to nonconvex constraints. However, for a recently proposed subclass of such methods that is built on the popular stochastic-gradient methodology from the unconstrained setting, convergence guarantees have been … Read more

    Enhancing explainability of stochastic programming solutions via scenario and recourse reduction

  • Tushar Rathi
  • Rishabh Gupta
  • Jose M. Pinto
  • Qi Zhang
    • Categories Stochastic Programming Tags , , ,

    Stochastic programming (SP) is a well-studied framework for modeling optimization problems under uncertainty. However, despite the significant advancements in solving large SP models, they are not widely used in industrial practice, often because SP solutions are difficult to understand and hence not trusted by the user. Unlike deterministic optimization models, SP models generally involve recourse … Read more