A Memoryless Augmented Gauss-Newton Method for Nonlinear Least-Squares Problems

Abstract

In this paper, we develop, analyze, and test a new algorithm for nonlinear least-squares problems. The algorithm uses a BFGS update of the Gauss-Newton Hessian when some hueristics indicate that the Gauss-Newton method may not make a good step. Some important elements are that the secant or quasi-Newton equations considered are not the obvious ones, and the method does not build up a Hessian approximation over several steps. The algorithm can be implemented easily as a modification of any Gauss-Newton code, and it seems to be useful for large residual problems.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 01, 1985
Accession Number
ADA454936

Entities

People

  • J. E. Dennis Jr.
  • Phuong A. Vu
  • Sheng Songbai

Organizations

  • Rice University

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Algorithms
  • Applied Mathematics
  • Availability
  • Classification
  • Contracts
  • Cooperation
  • Equations
  • Information Operations
  • Instructions
  • Mathematics
  • Monitoring
  • Residuals
  • Security
  • Standards

Fields of Study

  • Mathematics

Readers

  • Approximation Theory.
  • Operations Research