Introduction to the theory of optimization in Euclidean space / Samia Challal, Glendon College-York University, Toronto, Canada.

By: Challal, Samia [author.]Material type: TextTextSeries: Publisher: Boca Raton : CRC Press, Taylor & Francis Group, [2020]Copyright date: ©2020Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9780429203152; 0429203152; 9780429511738; 0429511736; 9780429515163; 0429515162Subject(s): Mathematical optimization | Euclidean algorithm | BUSINESS & ECONOMICS / Operations Research | MATHEMATICS / General | MATHEMATICS / Number SystemsDDC classification: 519.6 LOC classification: QA402.5Online resources: Taylor & Francis | OCLC metadata license agreement
Contents:
1. Introduction 1.1. Formulation of some optimization problems 1.2. Particular subsets of Rn 1.3. Functions of several variables 2. Unconstrained Optimization 2.1. Necessary condition 2.2. Classification of local extreme points 2.3. Convexity/concavity and global extreme points 2.4. Extreme value theorem 3. Constrained Optimization-Equality constraints 3.1. Tangent plane 3.2. Necessary condition for local extreme points-Equality constraints 3.3. Classification of local extreme points-Equality constraints 3.4. Global extreme points-Equality constraints 4. Constrained Optimization-Inequality constraints 4.1. Cone of feasible directions 4.2. Necessary condition for local extreme points/Inequality constraints 4.3. Classification of local extreme points-Inequality constraints 4.4. Global extreme points-Inequality constraints 4.5. Dependence on parameters
Summary: Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications. Students are taken progressively through the development of the proofs, where they have the occasion to practice tools of differentiation (Chain rule, Taylor formula) for functions of several variables in abstract situations. Throughout this book, students will learn the necessity of referring to important results established in advanced Algebra and Analysis courses. Features Rigorous and practical, offering proofs and applications of theorems Suitable as a textbook for advanced undergraduate students on mathematics or economics courses, or as reference for graduate-level readers Introduces complex principles in a clear, illustrative fashion
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"A Chapman & Hall book."

Introduction to the Theory of Optimization in Euclidean Space is intended to provide students with a robust introduction to optimization in Euclidean space, demonstrating the theoretical aspects of the subject whilst also providing clear proofs and applications. Students are taken progressively through the development of the proofs, where they have the occasion to practice tools of differentiation (Chain rule, Taylor formula) for functions of several variables in abstract situations. Throughout this book, students will learn the necessity of referring to important results established in advanced Algebra and Analysis courses. Features Rigorous and practical, offering proofs and applications of theorems Suitable as a textbook for advanced undergraduate students on mathematics or economics courses, or as reference for graduate-level readers Introduces complex principles in a clear, illustrative fashion

1. Introduction 1.1. Formulation of some optimization problems 1.2. Particular subsets of Rn 1.3. Functions of several variables 2. Unconstrained Optimization 2.1. Necessary condition 2.2. Classification of local extreme points 2.3. Convexity/concavity and global extreme points 2.4. Extreme value theorem 3. Constrained Optimization-Equality constraints 3.1. Tangent plane 3.2. Necessary condition for local extreme points-Equality constraints 3.3. Classification of local extreme points-Equality constraints 3.4. Global extreme points-Equality constraints 4. Constrained Optimization-Inequality constraints 4.1. Cone of feasible directions 4.2. Necessary condition for local extreme points/Inequality constraints 4.3. Classification of local extreme points-Inequality constraints 4.4. Global extreme points-Inequality constraints 4.5. Dependence on parameters

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