stochastic programming

REDUCTION OF TWO-STAGE PROBABILISTIC OPTIMIZATION PROBLEMS WITH DISCRETE DISTRIBUTION OF RANDOM DATA TO MIXED INTEGER PROGRAMMING PROBLEMS

  • Andrey Kibzun
  • Vladimir I. Norkin
  • Andrey Naumov
    • Categories (Mixed) Integer Nonlinear Programming, Nonsmooth Optimization, Stochastic Programming Tags , , , , ,

    We consider models of two-stage stochastic programming with a quantile second stage criterion and optimization models with a chance constraint on the second stage objective function values. Such models allow to formalize requirements to reliability and safety of the system under consideration, and to optimize the system in extreme conditions. We suggest a method of … Read more

    On reducing a quantile optimization problem with discrete distribution to a mixed integer programming problem

  • Andrey Kibzun
  • Vladimir I. Norkin
  • Andrey Naumov
    • Categories (Mixed) Integer Nonlinear Programming, Finance and Economics, Stochastic Programming Tags , , , , ,

    We suggest a method for equivalent transformation of a quantile optimization problem with discrete distribution of random parameters to mixed integer programming problems. The number of additional integer (in fact boolean) variables in the equivalent problems equals to the number of possible scenarios for random data. The obtained mixed integer problems are solved by standard … Read more

    Two methods of pruning Benders’ cuts and their application to the management of a gas portfolio

  • Romain Apparigliato
  • Laurent Pfeiffer
  • Sophie Auchapt
    • Categories Finance and Economics, Stochastic Programming Tags , , , ,

    In this article, we describe a gas portfolio management problem, which is solved with the SDDP (Stochastic Dual Dynamic Programming) algorithm. We present some improvements of this algorithm and focus on methods of pruning Benders’ cuts, that is to say, methods of picking out the most relevant cuts among those which have been computed. Our … Read more

    On parallelizing dual decomposition in stochastic integer programming

  • Miles Lubin
  • Kipp Martin
  • Burhaneddin Sandikçi
  • Cosmin G. Petra
    • Categories Nonsmooth Optimization, Parallel Algorithms, Stochastic Programming Tags , , , , ,

    For stochastic mixed-integer programs, we revisit the dual decomposition algorithm of Car\o{}e and Schultz from a computational perspective with the aim of its parallelization. We address an important bottleneck of parallel execution by identifying a formulation that permits the parallel solution of the \textit{master} program by using structure-exploiting interior-point solvers. Our results demonstrate the potential … Read more

    Threshold Boolean Form for Joint Probabilistic Constraints with Random Technology Matrix

  • Alexander Kogan
  • Miguel A. Lejeune
    • Categories (Mixed) Integer Nonlinear Programming, Data-Mining, Stochastic Programming Tags , , , , ,

    We develop a new modeling and exact solution method for stochastic programming problems that include a joint probabilistic constraint in which the multirow random technology matrix is discretely distributed. We binarize the probability distribution of the random variables in such a way that we can extract a threshold partially defined Boolean function (pdBf) representing the … Read more

    Dynamic sequencing and cut consolidation for the parallel hybrid-cut nested L-shaped method

  • Achim Koberstein
  • Christian Wolf
    • Categories Optimization Software and Modeling Systems, Stochastic Programming Tags , , ,

    The Nested L-shaped method is used to solve two- and multi-stage linear stochastic programs with recourse, which can have integer variables on the first stage. In this paper we present and evaluate a cut consolidation technique and a dynamic sequencing protocol to accelerate the solution process. Furthermore, we present a parallelized implementation of the algorithm, … Read more

    Computational aspects of risk-averse optimisation in two-stage stochastic models

  • Csaba I. Fábián
    • Categories Convex and Nonsmooth Optimization, Linear, Cone and Semidefinite Programming, Stochastic Programming Tags , , , , ,

    In this paper we argue for aggregated models in decomposition schemes for two-stage stochastic programming problems. We observe that analogous schemes proved effective for single-stage risk-averse problems, and for general linear programming problems. A major drawback of the aggregated approach for two-stage problems is that an aggregated master problem can not contain all the information … Read more

    Importance Sampling in Stochastic Programming: A Markov Chain Monte Carlo Approach

  • Ustun Berk
  • Webster Mort
  • Panos Parpas
  • Quang Kha Tran
    • Categories Stochastic Programming Tags , , , ,

    Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it requires the evaluation of a multidimensional integral whose integrand … Read more

    Multi-horizon stochastic programming

  • Marte Fodstad
  • Michal Kaut
  • Lars Hellemo
  • Kjetil T. Midthun
  • Asgeir Tomasgard
  • Adrian S. Werner
    • Categories Stochastic Programming Tags , , ,

    Infrastructure-planning models are challenging because of their combination of different time scales: while planning and building the infrastructure involves strategic decisions with time horizons of many years, one needs an operational time scale to get a proper picture of the infrastructure’s performance and profitability. In addition, both the strategic and operational levels are typically subject … Read more

    Stochastic First- and Zeroth-order Methods for Nonconvex Stochastic Programming

  • Saeed Ghadimi
  • Guanghui Lan
    • Categories Convex and Nonsmooth Optimization, Stochastic Programming, Unconstrained Optimization Tags , , , ,

    In this paper, we introduce a new stochastic approximation (SA) type algorithm, namely the randomized stochastic gradient (RSG) method, for solving an important class of nonlinear (possibly nonconvex) stochastic programming (SP) problems. We establish the complexity of this method for computing an approximate stationary point of a nonlinear programming problem. We also show that this … Read more