Adaptive algorithm for constrained least-squares problems

Z. F. Li, M. R. Osborne, T. Prvan

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

This paper is concerned with the implementation and testing of an algorithm for solving constrained least-squares problems. The algorithm is an adaptation to the least-squares case of sequential quadratic programming (SQP) trust-region methods for solving general constrained optimization problems. At each iteration, our local quadratic subproblem includes the use of the Gauss-Newton approximation but also encompasses a structured secant approximation along with tests of when to use this approximation. This method has been tested on a selection of standard problems. The results indicate that, for least-squares problems, the approach taken here is a viable alternative to standard general optimization methods such as the Byrd-Omojokun trust-region method and the Powell damped BFGS line search method.

Original languageEnglish
Pages (from-to)423-441
Number of pages19
JournalJournal of Optimization Theory and Applications
Volume114
Issue number2
DOIs
Publication statusPublished - Aug 2002
Externally publishedYes

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