Stochastic Programming

Nonanticipative duality, relaxations, and formulations for chance-constrained stochastic programs

  • Shabbir Ahmed
  • James R. Luedtke
  • Yongjia Song
  • Weijun Xie
    • Categories Stochastic Programming Tags , ,

    We propose two new Lagrangian dual problems for chance-constrained stochastic programs based on relaxing nonanticipativity constraints. We compare the strength of the proposed dual bounds and demonstrate that they are superior to the bound obtained from the continuous relaxation of a standard mixed-integer programming (MIP) formulation. For a given dual solution, the associated Lagrangian relaxation … Read more

    A scalable bounding method for multi-stage stochastic integer programs

  • Osman Y. Ozaltin
  • Burhaneddin Sandikçi
    • Categories Integer Programming, Parallel Algorithms, Stochastic Programming Tags , , , ,

    Many dynamic decision problems involving uncertainty can be appropriately modeled as multi-stage stochastic programs. However, most practical instances are so large and/or complex that it is impossible to solve them on a single computer, especially due to memory limitations. Extending the work of Sandikci et al. (2013) on two-stage stochastic mixed-integer-programs (SMIPs), this paper develops … Read more

    Stochastic Topology Design Optimization for Continuous Elastic Materials

  • Miguel Carrasco
  • Benjamin Ivorra
  • Ángel M. Ramos
    • Categories Mechanical Engineering, Multidisciplinary Design Optimization, Stochastic Programming

    In this paper, we develop a stochastic model for topology optimization. We find robust structures that minimize the compliance for a given main load having a stochastic behavior. We propose a model that takes into account the expected value of the compliance and its variance. We show that, similarly to the case of truss structures, … Read more

    Power-Capacity and Ramp-Capability Reserves for Wind Integration in Power-Based UC

  • Ross Baldick
  • Germán Morales-España
  • Javier García-González
  • Andres Ramos
    • Categories Applications - OR and Management Sciences, Robust Optimization, Stochastic Programming Tags , , , ,

    This paper proposes a power-based network-constrained unit commitment (UC) model as an alternative to the traditional deterministic UCs to deal with wind generation uncertainty. The formulation draws a clear distinction between power-capacity and ramp-capability reserves to deal with wind production uncertainty. These power and ramp requirements can be obtained from wind forecast information. The model … Read more

    An Accelerated Proximal Coordinate Gradient Method and its Application to Regularized Empirical Risk Minimization

  • Qihang Lin
  • Zhaosong Lu
  • Lin Xiao
    • Categories Applications - Science and Engineering, Convex and Nonsmooth Optimization, Stochastic Programming Tags , ,

    We consider the problem of minimizing the sum of two convex functions: one is smooth and given by a gradient oracle, and the other is separable over blocks of coordinates and has a simple known structure over each block. We develop an accelerated randomized proximal coordinate gradient (APCG) method for minimizing such convex composite functions. … Read more

    A Non-Parametric Structural Hybrid Modeling Approach for Electricity Prices

  • Ismael Arciniegas Rueda
  • Somayeh Moazeni
  • Michael Coulon
  • Warren B. Powell
  • Boyang Song
    • Categories Finance and Economics, Optimization Software and Modeling Systems, Stochastic Programming

    We develop a stochastic model of zonal/regional electricity prices, designed to reflect information in fuel forward curves and aggregated capacity and load as well as zonal or regional price spreads. We use a nonparametric model of the supply stack that captures heat rates and fuel prices for all generators in the market operator territory, combined … Read more

    Levenberg-Marquardt methods based on probabilistic gradient models and inexact subproblem solution, with application to data assimilation

  • Elhoucine Bergou
  • Serge Gratton
  • Luis Nunes Vicente
    • Categories Applications - Science and Engineering, Nonlinear Systems and Least-Squares, Stochastic Programming Tags , , , , , , ,

    The Levenberg-Marquardt algorithm is one of the most popular algorithms for the solution of nonlinear least squares problems. Motivated by the problem structure in data assimilation, we consider in this paper the extension of the classical Levenberg-Marquardt algorithm to the scenarios where the linearized least squares subproblems are solved inexactly and/or the gradient model is … Read more

    Symmetric confidence regions and confidence intervals for normal map formulations of stochastic variational inequalities

  • Shu Lu
    • Categories Complementarity and Variational Inequalities, Stochastic Programming Tags , , , , ,

    Stochastic variational inequalities (SVI) model a large class of equilibrium problems subject to data uncertainty, and are closely related to stochastic optimization problems. The SVI solution is usually estimated by a solution to a sample average approximation (SAA) problem. This paper considers the normal map formulation of an SVI, and proposes a method to build … Read more

    Individual confidence intervals for true solutions to stochastic variational inequalities

  • Amarjit Budhiraja
  • Shu Lu
  • Lamm Michael
    • Categories Complementarity and Variational Inequalities, Stochastic Programming

    Stochastic variational inequalities (SVI) provide a means for modeling various optimization and equilibrium problems where data are subject to uncertainty. Often it is necessary to estimate the true SVI solution by the solution of a sample average approximation (SAA) problem. This paper proposes three methods for building confidence intervals for components of the true solution, … Read more

    A Class of Randomized Primal-Dual Algorithms for Distributed Optimization

  • Jean-Christophe Pesquet
  • Audrey Repetti
    • Categories Convex Optimization, Nonsmooth Optimization, Stochastic Programming Tags , , , , , ,

    Based on a preconditioned version of the randomized block-coordinate forward-backward algorithm recently proposed in [Combettes,Pesquet,2014], several variants of block-coordinate primal-dual algorithms are designed in order to solve a wide array of monotone inclusion problems. These methods rely on a sweep of blocks of variables which are activated at each iteration according to a random rule, … Read more