Implementation of a robust SQP algorithm
This paper presents an implementation of a sequential quadratic programming (SQP) algorithm for the solution of nonlinear programming (NLP) problems. In the proposed algorithm, a solution to the NLP problem is found by minimizing the L 1 exact penalty function. The search direction for the penalty f...
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| Vydáno v: | Optimization methods & software Ročník 23; číslo 6; s. 827 - 846 |
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| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Taylor & Francis
01.12.2008
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| Témata: | |
| ISSN: | 1055-6788, 1029-4937 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | This paper presents an implementation of a sequential quadratic programming (SQP) algorithm for the solution of nonlinear programming (NLP) problems. In the proposed algorithm, a solution to the NLP problem is found by minimizing the L
1
exact penalty function. The search direction for the penalty function minimization is determined by solving a strictly convex quadratic programming (QP) problem. Here, we make the basic SQP algorithm more robust (i) by solving a relaxed, strictly convex, QP problem in cases where the constraints are inconsistent, (ii) by performing a non-monotone line search to improve efficiency, and (iii) by using second-order corrections to avoid the Maratos effect. The robustness of the algorithm is demonstrated via a C language implementation that is applied to numerous parameter optimization and optimal control problems that have appeared in the literature. The results obtained show that both non-monotone line searches and second-order corrections can significantly reduce the amount of work required to solve parameter optimization problems. |
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| ISSN: | 1055-6788 1029-4937 |
| DOI: | 10.1080/10556780801996228 |