Adaptive Algorithm for Constrained Least-Squares Problems

Z.F. Li, M.R. Osborne and T. Prvan
Additional contact information
Z.F. Li: Australian National University
M.R. Osborne: Australian National University
T. Prvan: University of Canberra

Journal of Optimization Theory and Applications, 2002, vol. 114, issue 2, No 8, 423-441

Abstract: 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.

Keywords: constrained optimization; nonlinear least squares; SQP methods; Gauss–Newton approximation; quasi-Newton method (search for similar items in EconPapers)
Date: 2002
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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DOI: 10.1023/A:1016043919978

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