Large-Scale Stochastic Linear Programs: Importance Sampling and Benders Decomposition

Abstract

The paper demonstrates how large-scale stochastic linear programs with recourse can be efficiently solved by using a blending of classical Benders decomposition with a relatively new technique called importance sampling. Numerical results of large-scale problems in the area of expansion planning of power systems and financial planning are presented.

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1991
Accession Number
ADA234962

Entities

People

  • George Bernard Dantzig
  • Gerd Infanger

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Applied Mathematics
  • Blending
  • Computational Science
  • Computations
  • Computer Programming
  • Computers
  • Decomposition
  • Discrete Distribution
  • Estimators
  • Linear Programming
  • Mathematical Programming
  • Mathematics
  • Operations Research
  • Sampling
  • Transmission Lines

Readers

  • Calculus or Mathematical Analysis
  • Computational Modeling and Simulation
  • Computer Engineering