A Strategy for Global Convergence in a Sequential Quadratic Programming Algorithm

In a previous work [P. Boggs and J. Tolle, SIAM J. Numer. Anal., 21 (1984), pp. 1146-1161], the authors introduced a merit function for use with the sequential quadratic programming (SQP) algorithm for solving nonlinear programming problems. Here, further theoretical justification, including a globa...

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Veröffentlicht in:SIAM journal on numerical analysis Jg. 26; H. 3; S. 600 - 623
Hauptverfasser: Boggs, Paul T., Tolle, Jon W.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia, PA Society for Industrial and Applied Mathematics 01.06.1989
Schlagworte:
Algorithms
Applied mathematics
Applied sciences
Approximation
Constrained optimization
Exact sciences and technology
Lagrange multiplier
Lagrangian function
Mathematical functions
Mathematical programming
Nonlinear programming
Operational research and scientific management
Operational research. Management science
Perceptron convergence procedure
Quadratic programming
Scalars
Quadratic programming
Algorithm
Convergence
Mathematical programming
ISSN:0036-1429, 1095-7170
Online-Zugang:Volltext
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Zusammenfassung:In a previous work [P. Boggs and J. Tolle, SIAM J. Numer. Anal., 21 (1984), pp. 1146-1161], the authors introduced a merit function for use with the sequential quadratic programming (SQP) algorithm for solving nonlinear programming problems. Here, further theoretical justification, including a global convergence theorem, is provided. In addition, modifications are suggested that allow the efficient implementation of the merit function while maintaining the important convergence properties. Numerical results are presented demonstrating the effectiveness of the procedure.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:0036-1429
1095-7170
DOI:10.1137/0726036