Introduction to the Theory of Optimization in Euclidean Space / Edition 1

Introduction to the Theory of Optimization in Euclidean Space / Edition 1

by Samia Challal
ISBN-10:
0367195577
ISBN-13:
9780367195571
Pub. Date:
11/14/2019
Publisher:
CRC Press
ISBN-10:
0367195577
ISBN-13:
9780367195571
Pub. Date:
11/14/2019
Publisher:
CRC Press
Introduction to the Theory of Optimization in Euclidean Space / Edition 1

Introduction to the Theory of Optimization in Euclidean Space / Edition 1

by Samia Challal
$140.0
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Overview

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

Product Details

ISBN-13: 9780367195571
Publisher: CRC Press
Publication date: 11/14/2019
Series: Chapman & Hall/CRC Series in Operations Research
Pages: 334
Product dimensions: 6.12(w) x 9.19(h) x (d)

About the Author

Samia Challal is an assistant professor of Mathematics at Glendon College, the bilingual campus of York University. Her research interests include, homogenization, optimization, free boundary problems, partial differential equations, and problems arising from mechanics.

Table of 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. 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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