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Basic mathematical programming theory

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Basic mathematical programming theory/ by Giorgio Giorgi, Bienvenido Jimenez, Vicente Novo.
作者:	Giorgi, Giorgio.
其他作者:	Jimenez, Bienvenido.
出版者:	Cham :Springer International Publishing : : 2023.,
面頁冊數:	xii, 433 p. :ill., digital ;24 cm.
內容註:	Preface -- Chapter 1. Basic Notions and Definitions -- 1.1. Introduction -- 1.2. Basic Notions of Analysis and Linear Algebra -- 1.3. Basic Notions and Properties of Optimization Problems -- Chapter 2. Elements of Convex Analysis. Theorems of the Alternative for Linear Systems. Tangent Cones -- 2.1. Elements of Convex Analysis -- 2.2. Theorems of the Alternative for Linear Systems -- 2.3. Tangent Cones -- Chapter 3. Convex Functions and Generalized Convex Functions -- 3.1. Convex Functions -- 3.2. Generalized Convex Functions -- 3.3. Optimality Properties of Convex and Generalized Convex Functions. Theorems of the Alternative for Nonlinear Systems -- Chapter 4. Unconstrained Optimization Problems. Set-Constrained Optimization Problems. Classical Constrained Optimization Problems -- 4.1. Unconstrained Optimization Problems -- 4.2. Set-Constrained Optimization Problems -- 4.3. Optimization Problems with Equality Constraints ("Classical Constrained Optimization Problems") -- Chapter 5. Constrained Optimization Problems with Inequality Constraints -- 5.1. First-Order Conditions -- 5.2. Constraint Qualifications -- 5.3. Second-Order Conditions -- 5.4. Other Formulations of the Problem. Some Examples -- Chapter 6. Constrained Optimization Problems with Mixed Constraints -- 6.1. First-Order Conditions -- 6.2. Constraint Qualifications -- 6.3. Second-Order Conditions -- 6.4. Problems with a Set Constraint. Asymptotic Optimality Conditions -- Chapter 7.Sensitivity Analysis -- 7.1. General Results -- 7.2. Sensitivity Results for Right-Hand Side Perturbations -- Chapter 8. Convex Optimization: Saddle Points Characterization and Introduction to Duality -- 8.1. Convex Optimization: Saddle Points Characterization -- 8.2. Introduction to Duality -- Chapter 9. Linear Programming and Quadratic Programming -- 9.1. Linear Programming -- 9.2. Duality for Linear Programming -- 9.3. Quadratic Programming -- Chapter 10. Introduction to Nonsmooth Optimization Problems -- 10.1. The Convex Case -- 10.2. The Lipschitz Case -- 10.3. The Axiomatic Approach of K.-H. Elster and J. Thierfelder to Nonsmooth Optimization -- Chapter 11. Introduction to Multiobjective Optimization -- 11.1. Optimality Notions -- 11.2. The Weighted Sum Method and Optimality Conditions -- References -- Index.
Contained By:	Springer Nature eBook
標題:	Programming (Mathematics) -
電子資源:	https://doi.org/10.1007/978-3-031-30324-1
ISBN:	9783031303241

Basic mathematical programming theory
Giorgi, Giorgio.

Basic mathematical programming theory[electronic resource] /by Giorgio Giorgi, Bienvenido Jimenez, Vicente Novo. - Cham :Springer International Publishing :2023. - xii, 433 p. :ill., digital ;24 cm. - International series in operations research & management science,v. 3442214-7934 ;. - International series in operations research & management science ;v. 344..

Preface -- Chapter 1. Basic Notions and Definitions -- 1.1. Introduction -- 1.2. Basic Notions of Analysis and Linear Algebra -- 1.3. Basic Notions and Properties of Optimization Problems -- Chapter 2. Elements of Convex Analysis. Theorems of the Alternative for Linear Systems. Tangent Cones -- 2.1. Elements of Convex Analysis -- 2.2. Theorems of the Alternative for Linear Systems -- 2.3. Tangent Cones -- Chapter 3. Convex Functions and Generalized Convex Functions -- 3.1. Convex Functions -- 3.2. Generalized Convex Functions -- 3.3. Optimality Properties of Convex and Generalized Convex Functions. Theorems of the Alternative for Nonlinear Systems -- Chapter 4. Unconstrained Optimization Problems. Set-Constrained Optimization Problems. Classical Constrained Optimization Problems -- 4.1. Unconstrained Optimization Problems -- 4.2. Set-Constrained Optimization Problems -- 4.3. Optimization Problems with Equality Constraints ("Classical Constrained Optimization Problems") -- Chapter 5. Constrained Optimization Problems with Inequality Constraints -- 5.1. First-Order Conditions -- 5.2. Constraint Qualifications -- 5.3. Second-Order Conditions -- 5.4. Other Formulations of the Problem. Some Examples -- Chapter 6. Constrained Optimization Problems with Mixed Constraints -- 6.1. First-Order Conditions -- 6.2. Constraint Qualifications -- 6.3. Second-Order Conditions -- 6.4. Problems with a Set Constraint. Asymptotic Optimality Conditions -- Chapter 7.Sensitivity Analysis -- 7.1. General Results -- 7.2. Sensitivity Results for Right-Hand Side Perturbations -- Chapter 8. Convex Optimization: Saddle Points Characterization and Introduction to Duality -- 8.1. Convex Optimization: Saddle Points Characterization -- 8.2. Introduction to Duality -- Chapter 9. Linear Programming and Quadratic Programming -- 9.1. Linear Programming -- 9.2. Duality for Linear Programming -- 9.3. Quadratic Programming -- Chapter 10. Introduction to Nonsmooth Optimization Problems -- 10.1. The Convex Case -- 10.2. The Lipschitz Case -- 10.3. The Axiomatic Approach of K.-H. Elster and J. Thierfelder to Nonsmooth Optimization -- Chapter 11. Introduction to Multiobjective Optimization -- 11.1. Optimality Notions -- 11.2. The Weighted Sum Method and Optimality Conditions -- References -- Index.

This book presents a unified, progressive treatment of the basic mathematical tools of mathematical programming theory. The subject of (static) optimization, also called mathematical programming, is one of the most important and widespread branches of modern mathematics, serving as a cornerstone of such scientific subjects as economic analysis, operations research, management sciences, engineering, chemistry, physics, statistics, computer science, biology, and social sciences. This book presents a unified, progressive treatment of the basic mathematical tools of mathematical programming theory. The authors expose said tools, along with results concerning the most common mathematical programming problems formulated in a finite-dimensional setting, forming the basis for further study of the basic questions on the various algorithmic methods and the most important particular applications of mathematical programming problems. This book assumes no previous experience in optimization theory, and the treatment of the various topics is largely self-contained. Prerequisites are the basic tools of differential calculus for functions of several variables, the basic notions of topology in Rn and of linear algebra, and the basic mathematical notions and theoretical background used in analyzing optimization problems. The book is aimed at both undergraduate and postgraduate students interested in mathematical programming problems but also those professionals who use optimization methods and wish to learn the more theoretical aspects of these questions.

ISBN: 9783031303241

Standard No.: 10.1007/978-3-031-30324-1doiSubjects--Topical Terms:

547124
Programming (Mathematics)

LC Class. No.: QA402.5

Dewey Class. No.: 519.7

Basic mathematical programming theory
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520 $a This book presents a unified, progressive treatment of the basic mathematical tools of mathematical programming theory. The subject of (static) optimization, also called mathematical programming, is one of the most important and widespread branches of modern mathematics, serving as a cornerstone of such scientific subjects as economic analysis, operations research, management sciences, engineering, chemistry, physics, statistics, computer science, biology, and social sciences. This book presents a unified, progressive treatment of the basic mathematical tools of mathematical programming theory. The authors expose said tools, along with results concerning the most common mathematical programming problems formulated in a finite-dimensional setting, forming the basis for further study of the basic questions on the various algorithmic methods and the most important particular applications of mathematical programming problems. This book assumes no previous experience in optimization theory, and the treatment of the various topics is largely self-contained. Prerequisites are the basic tools of differential calculus for functions of several variables, the basic notions of topology in Rn and of linear algebra, and the basic mathematical notions and theoretical background used in analyzing optimization problems. The book is aimed at both undergraduate and postgraduate students interested in mathematical programming problems but also those professionals who use optimization methods and wish to learn the more theoretical aspects of these questions.
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