Sequential quadratic programming for large-scale nonlinear optimization

The sequential quadratic programming (SQP) algorithm has been one of the most successful general methods for solving nonlinear constrained optimization problems. We provide an introduction to the general method and show its relationship to recent developments in interior-point approaches, emphasizin...

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Bibliographic Details
Published inJournal of computational and applied mathematics Vol. 124; no. 1; pp. 123 - 137
Main Authors Boggs, Paul T., Tolle, Jon W.
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.12.2000
Subjects
Global convergence
Interior-point methods
Local
Newton methods
Nonlinear optimization
Sequential quadratic programming
Trust-region methods convergence
Global convergence
Sequential quadratic programming
Newton methods
Nonlinear optimization
Local
Interior-point methods
Trust-region methods convergence
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Summary:The sequential quadratic programming (SQP) algorithm has been one of the most successful general methods for solving nonlinear constrained optimization problems. We provide an introduction to the general method and show its relationship to recent developments in interior-point approaches, emphasizing large-scale aspects.
ISSN:0377-0427
1879-1778
DOI:10.1016/S0377-0427(00)00429-5