Practical Augmented Lagrangian Methods for Constrained Optimization

Häftad, Engelska, 2014

866 kr

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Beskrivning

This book focuses on Augmented Lagrangian techniques for solving practical constrained optimization problems. The authors rigorously delineate mathematical convergence theory based on sequential optimality conditions and novel constraint qualifications. They also orient the book to practitioners by giving priority to results that provide insight on the practical behavior of algorithms and by providing geometrical and algorithmic interpretations of every mathematical result, and they fully describe a freely available computational package for constrained optimization and illustrate its usefulness with applications.

Produktinformation

Utgivningsdatum:2014-08-30
Mått:152 x 229 x 11 mm
Vikt:430 g
Format:Häftad
Språk:Engelska
Serie:Fundamentals of Algorithms
Antal sidor:233
Förlag:Society for Industrial & Applied Mathematics,U.S.
ISBN:9781611973358

Utforska kategorier

Optimering inom Naturvetenskap och teknik

Mer om författaren

Ernesto Birgin is a professor in the Department of Computer Science at the Institute of Mathematics and Statistics of the University of São Paulo. He is a member of the editorial boards of the Journal of Global Optimization, Computational and Applied Mathematics, the Bulletin of Computational Applied Mathematics, Pesquisa Operacional, and Trends in Applied and Computational Mathematics. He has published over 50 papers on computational optimization and applications. José Mario Martínez is a professor in the Department of Applied Mathematics at the University of Campinas, Brazil. He is a member of the Brazilian Academy of Sciences, former Editor in Chief of Computational and Applied Mathematics, a member of the editorial board of Numerical Algorithms, and the author of over 150 papers on numerical mathematics, optimization, and applications.

Innehållsförteckning

Chapter 1: Introduction Chapter 2: Practical MotivationsChapter 3: Optimality ConditionsChapter 4: Model Augmented Lagrangian AlgorithmChapter 5: Global Minimization ApproachChapter 6: General Affordable AlgorithmsChapter 7: Boundedness of the Penalty ParametersChapter 8: Solving Unconstrained SubproblemsChapter 9: Solving Constrained SubproblemsChapter 10: First Approach to AlgencanChapter 11: Adequate Choice of SubroutinesChapter 12: Making a Good Choice of Algorithmic Options and ParametersChapter 13: Practical ExamplesChapter 14: Final Remarks