Confidence Regions for Global Optima in Nonlinear Programming.

Abstract

The report is concerned with developing new statistical techniques for nonlinear optimization including nonconvex optimization. The approach is a statistical one and provides an upper confidence limit for the global maximum of a mathematical function g(x) of a vector x in a multi-dimensional 'feasible space', say S. Specifically the report develops the statistical techniques for determining these confidence limits as well as algorithms implementing the techniques and computer programs executing the algorithms. (Modified author abstract)

Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1973
Accession Number
AD0767700

Entities

People

  • Herman O. Hartley
  • Robert L. Sielken Jr.
  • Ta-lin Liau

Organizations

  • Texas A&M University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Application Software
  • Computer Programming
  • Computer Programs
  • Computers
  • Confidence Limits
  • Digital Information
  • Evolutionary Algorithms
  • Heuristic Methods
  • Mathematics
  • Nonlinear Programming
  • Optimization

Fields of Study

  • Mathematics

Readers

  • Computer Programming and Software Development.
  • Control Systems Engineering.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Space