Stochastic Semidefinite Programming: Applications and Algorithms

Abstract

Stochastic semidefinite programs (SSDP's) are a new class of optimization problems with a wide variety applications proposed by the PI and his doctoral students. The broad objective of this project was to develop applications of and algorithms for SSDP's. We have developed five classes of novel applications, and three classes of new algorithms. We have proved the convergence and polynomial complexity of the algorithms. We have also identified two new classes of optimization problems which may be useful for future research.

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Document Details

Document Type
Technical Report
Publication Date
Mar 03, 2012
Accession Number
ADA573242

Entities

People

  • K. A. Ariyawansa

Organizations

  • Washington State University

Tags

Communities of Interest

  • Human Systems

DTIC Thesaurus Topics

  • Algorithms
  • Computational Complexity
  • Computations
  • Computer Programming
  • Department Of Defense
  • Engineering
  • Inequalities
  • Linear Programming
  • Mathematics
  • Optimization
  • Parallel Computing
  • Parallel Processing
  • Polynomials
  • Probability
  • Probability Distributions
  • Semidefinite Programming
  • Students

Fields of Study

  • Engineering

Readers

  • Operations Research
  • Research Science/Academic Research