Solving Stochastic Linear Programs on a Hypercube Multicomputer.

Abstract

Large-scale stochastic linear programs can be efficiently solved by using a blending of classical Benders decomposition and a relatively new technique called importance sampling. The paper demonstrates how such an approach can be effectively implemented on a parallel (Hypercube) multicomputer. Numerical results are presented.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1991
Accession Number
ADA240443

Entities

People

  • George Bernard Dantzig
  • Gerd Infanger
  • James K. Ho

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computations
  • Computer Programming
  • Computers
  • Linear Programming
  • Mathematical Programming
  • Mathematics
  • Operating Systems
  • Operations Research
  • Parallel Computing
  • Parallel Processing
  • Parallel Processors
  • Probability
  • Probability Distributions
  • Sampling
  • Universities

Readers

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  • Operations Research