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Introduction to the Theory of Optimization in Euclidean Space Vital Source e-bog
Samia Challal
(2019)
Taylor & Francis
669,00 kr.
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Introduction to Theory of Optimization in Euclidean Space
Samia Challal
(2019)
Sprog: Engelsk
CRC Press LLC
1.499,00 kr.
Denne bog er endnu ikke udgivet. Den forventes Nov 2019.
Detaljer om varen
- 1. Udgave
- Vital Source searchable e-book (Reflowable pages)
- Udgiver: Taylor & Francis (November 2019)
- ISBN: 9780429515163
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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Bookshelf online: 5 år fra købsdato.
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Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: ubegrænset dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- Hardback: 318 sider
- Udgiver: CRC Press LLC (November 2019)
- ISBN: 9780367195571
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
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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