Rapidly Convergent Algorithms for Nonsmooth Optimization.

Abstract

The research supported under this grant has led to new developments for solving nonlinear optimization problems involving functions that are not everywhere differentiable and/or are implicitly defined, such as those that arise from dual formulations of optimization models. A new result has been proved showing rapid convergence of an algorithm for the single variable case where generalized derivations are available. For the single variable case where only function values are used a safeguarded bracketing technique has been introduced which guarantees convergence for lower semicontinous functions and preserves rapid convergence of polyhedral and/or polynomial fitting algorithms. A safeguarded polyhedral/quadratic fitting algorithm has been developed which has better than linear convergence for certain piecewise twice continously differentiable functions. Ideas from the single variable case are being extended to the multi-variable case. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Jul 14, 1986
Accession Number
ADA182531

Entities

People

  • Robert Mifflin

Organizations

  • Washington State University

Tags

DTIC Thesaurus Topics

  • Air Force
  • Air Force Facilities
  • Algorithms
  • Computations
  • Computer Programming
  • Convergence
  • Heuristic Methods
  • Nonlinear Programming
  • Optimization
  • Parallel Computing
  • Parallel Processing
  • Scientific Research
  • Security
  • Sequences
  • Universities
  • Weak Convergence

Readers

  • Operations Research