A Practical Algorithm for General Large Scale Nonlinear Optimization Problems

Authors:
Paul T. Boggs;Anthony J. Kearsley;Jon W. Tolle
Affiliations:
-;-;-
Venue:
SIAM Journal on Optimization
Year:
1999

Citing 0
Cited 6

Global solutions for nonlinear systems using qualitative reasoning

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A New Trust-Region Algorithm for Equality Constrained Optimization

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Quadratic surface reconstruction from multiple views using SQP

Integrated image and graphics technologies
Implementation of a robust SQP algorithm

Optimization Methods & Software
BFGS trust-region method for symmetric nonlinear equations

Journal of Computational and Applied Mathematics
A Second Derivative SQP Method: Global Convergence

SIAM Journal on Optimization

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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.