Solution Methods for Stochastic Dynamic Linear Programs.

Abstract

Linear programs have been formulated for many practical situations that require decisions made periodically through time. These dynamic linear programs often involve uncertainties. Deterministic solutions of these problems may lead to costly incorrect decisions, and, when a stochastic solution is attempted the problem may become too large. In this report, we present methods for reducing the computational cost of these stochastic programs, and we show conditions under which the stochastic program need not be solved. Our methods are based on the large-scale programming techniques of decomposition, partitioning, and basic factorization. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1980
Accession Number
ADA096119

Entities

People

  • John R. Birge

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I
  • Engineered Resilient Systems
  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computations
  • Computer Programming
  • Decomposition
  • Discrete Distribution
  • Dynamic Programming
  • Linear Programming
  • Mathematical Programming
  • New York
  • Operations Research
  • Optimization
  • Probability
  • Random Variables
  • Simplex Method
  • Standards
  • Uncertainty

Fields of Study

  • Mathematics

Readers

  • Computational Fluid Dynamics (CFD)
  • Mathematical Modeling and Probability Theory.
  • Theoretical Analysis.