stochastic decomposition

Sampling-based Decomposition Algorithms for Multistage Stochastic Programming

  • Harsha Gangammanavar
    • Categories Stochastic Programming Tags , , ,

    Sampling-based algorithms provide a practical approach to solving large-scale multistage stochastic programs. This chapter presents two alternative approaches to incorporating sampling within multistage stochastic linear programming algorithms. In the first approach, sampling is used to construct a sample average approximation (SAA) of the true multistage program. Subsequently, an optimization step is undertaken using deterministic decomposition … Read more

    Stochastic Dynamic Linear Programming: A Sequential Sampling Algorithm for Multistage Stochastic Linear Programming

  • Harsha Gangammanavar
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , , ,

    Multistage stochastic programming deals with operational and planning problems that involve a sequence of decisions over time while responding to realizations that are uncertain. Algorithms designed to address multistage stochastic linear programming (MSLP) problems often rely upon scenario trees to represent the underlying stochastic process. When this process exhibits stagewise independence, sampling-based techniques, particularly the … Read more

    Stochastic Decomposition for Two-stage Stochastic Linear Programs with Random Cost Coefficients

  • Harsha Gangammanavar
  • Yifan Liu
  • Suvrajeet Sen
    • Categories Stochastic Programming Tags , , , ,

    Stochastic decomposition (SD) has been a computationally effective approach to solve large-scale stochastic programming (SP) problems arising in practical applications. By using incremental sampling, this approach is designed to discover an appropriate sample size for a given SP instance, thus precluding the need for either scenario reduction or arbitrary sample sizes to create sample average … Read more

    Learning Enabled Optimization: Towards a Fusion of Statistical Learning and Stochastic Optimization

  • Yunxiao Deng
  • Suvrajeet Sen
    • Categories Problem Solving Environments, Statistics, Stochastic Programming Tags , , ,

    Several emerging applications, such as “Analytics of Things” and “Integrative Analytics” call for a fusion of statistical learning (SL) and stochastic optimization (SO). The Learning Enabled Optimization paradigm fuses concepts from these disciplines in a manner which not only enriches both SL and SO, but also provides a framework which supports rapid model updates and … Read more

    Mitigating Uncertainty via Compromise Decisions in Two-stage Stochastic Linear Programming

  • Suvrajeet Sen
  • Yifan Liu
    • Categories Stochastic Programming Tags , ,

    Stochastic Programming (SP) has long been considered as a well-justified yet computationally challenging paradigm for practical applications. Computational studies in the literature often involve approximating a large number of scenarios by using a small number of scenarios to be processed via deterministic solvers, or running Sample Average Approximation on some genre of high performance machines … Read more