PROGRAMMING UNDER UNCERTAINTY AND STOCHASTIC OPTIMAL CONTROL

Abstract

The theory of programming under uncertainty is extended to the case when the decision variables are elements of a Banach space. This approach leads to a very natural application of the computational techniques of mathematical programming to stochastic optimal control problems. It is shown that there exists an equivalent deterministic mathematical program whose set of feasible solutions is a convex set and whose objective function can be expressed as a convex function of the initial decision variables. In the second part, a duality theory is developed for this class of problems and some of the relations to the maximum principle for stochastic linear control problems are given.

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Document Details

Document Type
Technical Report
Publication Date
Apr 01, 1965
Accession Number
AD0618201

Entities

People

  • Richard Van Slyke
  • Roger J-B Wets

Organizations

  • Boeing

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Applied Mathematics
  • Banach Space
  • Computer Programming
  • Differential Equations
  • Distribution Functions
  • Equations
  • Linear Differential Equations
  • Linear Programming
  • Mathematics
  • Operations Research
  • Optimization
  • Probability
  • Probability Distribution Functions
  • Probability Distributions
  • Random Variables
  • Time Intervals
  • Uncertainty

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.

Technology Areas

  • Space