Parallel Processors for Planning Under Uncertainty

Abstract

This paper describes joint research under way whose goal is to demonstrate, for an important class of multistage stochastic models, that a variety of techniques for solving large-scale linear programs can be effectively mixed to attack this fundamental problem. The ideas involve nested primal and dual decomposition, combined with Monte Carlo simulation, high speed importance sampling, and quadrature methods for numerical integration, together with the use parallel processors. Keywords: Linear programming, Mathematical programming, Large-scale optimization, Deterministic models, Times-staged systems, Staircase systems, Decomposition principle, Benders decomposition, Cutting planes, Parallel processors, Stochastic systems, Reliable systems, Hedging, Monte Carlo simulation, Importance sampling.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1988
Accession Number
ADA199067

Entities

People

  • George Bernard Dantzig
  • Peter W. Glynn

Organizations

  • Stanford University

Tags

Communities of Interest

  • Air Platforms
  • C4I
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Aircrafts
  • Computational Science
  • Computer Programming
  • Data Analysis
  • Decomposition
  • Linear Programming
  • Mathematical Programming
  • Monte Carlo Method
  • Operations Research
  • Optimization
  • Parallel Processors
  • Probability
  • Probability Distributions
  • Random Variables
  • Sampling
  • Simulations
  • Statistical Sampling

Fields of Study

  • Engineering

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Operations Research
  • Statistical inference.