Rapidly Convergent Algorithms for Nonsmooth Optimization.

Abstract

The research supported by this grant has continued the development of efficient methods for solving optimization problems involving implicitly defined functions that are not everywhere differentiable. Research on a rapidly convergent algorithm for the constrained single variable case where generalized derivatives are known has been completed. Significant process has been made in extending this work to the n-variable case via the definition of better than linear convergence. Safeguarding techniques have been developed which ensure first order convergence on problems with semismooth functions, but do not prevent better than linear convergence on piecewise second order smooth functions. For the constrained case a scale-free automatic penalty technique has been devised. A new stable method for solving certain quadratic programming problems has been developed which includes a technique for resolving degeneracy. (JHD)

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Document Details

Document Type
Technical Report
Publication Date
Dec 15, 1988
Accession Number
ADA207629

Entities

People

  • Robert Mifflin

Organizations

  • Washington State University

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • C4I

DTIC Thesaurus Topics

  • Algorithms
  • Applied Mathematics
  • Automatic
  • Availability
  • Computer Programming
  • Convergence
  • Direction Finding
  • Mathematical Programming
  • Mathematics
  • New York
  • Nonlinear Programming
  • Operations Research
  • Optimization
  • Parallel Computing
  • Parallel Processing
  • Quadratic Programming
  • Theses

Readers

  • Operations Research