Nonlinear programming : theory and algorithms / Mokhtar S. Bazaraa, Hanif D. Sherali, C.M. Shetty
Tipo de material:
TextoDetalles de publicación: Hoboken : Wiley-Interscience, 2006 Edición: 3rd ed.Descripción: XV, 853 p. ; 25 cmISBN: 0-471-48600-0Tema(s): Optimización matemática | Programación no linealResumen: This book presents recent developments of key topics in nonlinear programming (NLP) using a logical and self-contained format. The volume is divided into three sections: convex analysis, optimality conditions, and dual computational techniques. Precise statements of algortihms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations, and numerous exercises to aid readers in understanding the concepts and methods discussed.Resumen: Índice: Chapter 1. Introduction. PART I: CONVEX ANALYSIS. Chapter 2. Convex Sets. Chapter 3. Convex Functions and Generalizations. PART II: OPTIMALITY CONDITIONS AND DUALITY. Chapter 4. The Fritz John and the Karush-Kuhn-Tucker Optimality Conditions. Chapter 5. Constraint Qualifications. Chapter 6. Lagrangian Duality and Saddle Point Optimality Conditions. PART III: ALGORITHMS AND THEIR CONVERGENCE. Chapter 7. The Concept of an Algorithm. Chapter 8. Unconstrained Optimization. Chapter 9. Penalty and Barrier Functions. Chapter 10. Methods of Feasible Directions. Chapter 11. Linear Complementary Problem, and Quadrat... Etc.
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Bibliografía: p. 779-841
This book presents recent developments of key topics in nonlinear programming (NLP) using a logical and self-contained format. The volume is divided into three sections: convex analysis, optimality conditions, and dual computational techniques. Precise statements of algortihms are given along with convergence analysis. Each chapter contains detailed numerical examples, graphical illustrations, and numerous exercises to aid readers in understanding the concepts and methods discussed.
Índice: Chapter 1. Introduction. PART I: CONVEX ANALYSIS. Chapter 2. Convex Sets. Chapter 3. Convex Functions and Generalizations. PART II: OPTIMALITY CONDITIONS AND DUALITY. Chapter 4. The Fritz John and the Karush-Kuhn-Tucker Optimality Conditions. Chapter 5. Constraint Qualifications. Chapter 6. Lagrangian Duality and Saddle Point Optimality Conditions. PART III: ALGORITHMS AND THEIR CONVERGENCE. Chapter 7. The Concept of an Algorithm. Chapter 8. Unconstrained Optimization. Chapter 9. Penalty and Barrier Functions. Chapter 10. Methods of Feasible Directions. Chapter 11. Linear Complementary Problem, and Quadrat... Etc.
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