A Practical Algorithm For General Large Scale Nonlinear Optimization Problems (1994)
| Venue: | SIAM Journal on Optimization |
| Citations: | 22 - 10 self |
BibTeX
@INPROCEEDINGS{Boggs94apractical,
author = {Paul T. Boggs and Anthony J. Kearsley and Jon and Jon W. Tolle},
title = {A Practical Algorithm For General Large Scale Nonlinear Optimization Problems},
booktitle = {SIAM Journal on Optimization},
year = {1994},
pages = {755--778}
}
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Abstract
. We provide an effective and efficient implementation of a sequential quadratic programming (SQP) algorithm for the general large scale nonlinear programming problem. In this algorithm the quadratic programming subproblems are solved by an interior point method that can be prematurely halted by a trust region constraint. Numerous computational enhancements to improve the numerical performance are presented. These include a dynamic procedure for adjusting the merit function parameter and procedures for adjusting the trust region radius. Numerical results and comparisons are presented. Key words: nonlinear programming, interior point, SQP, merit function, trust region, large scale 1. Introduction. In a series of recent papers, [3], [6], and [8], the authors have developed a new algorithmic approach for solving large, nonlinear, constrained optimization problems. This proposed procedure is, in essence, a sequential quadratic programming (SQP) method that uses an interior point algorithm...
