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Nonlinear optimization

Aragon, Francisco J.

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Nonlinear optimization

Record Type:	Electronic resources : Monograph/item
Title/Author:	Nonlinear optimization/ by Francisco J. Aragon ... [et al.].
other author:	Aragon, Francisco J.
Published:	Cham :Springer International Publishing : : 2019.,
Description:	xiv, 350 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Preliminaries -- Part I. Analytical Optimization -- 2. Convexity -- 3. Unconstrained Optimization -- 4. Convex Optimization -- Part II. Numerical Optimization -- 5. Unconstrained Optimization Algorithms -- 6. Constrained Optimization -- Solutions to selected exercises -- References -- Index.
Contained By:	Springer eBooks
Subject:	Mathematical optimization. -
Online resource:	https://doi.org/10.1007/978-3-030-11184-7
ISBN:	9783030111847

Nonlinear optimization
Nonlinear optimization[electronic resource] /by Francisco J. Aragon ... [et al.]. - Cham :Springer International Publishing :2019. - xiv, 350 p. :ill., digital ;24 cm. - Springer undergraduate texts in mathematics and technology,1867-5506. - Springer undergraduate texts in mathematics and technology..

1. Preliminaries -- Part I. Analytical Optimization -- 2. Convexity -- 3. Unconstrained Optimization -- 4. Convex Optimization -- Part II. Numerical Optimization -- 5. Unconstrained Optimization Algorithms -- 6. Constrained Optimization -- Solutions to selected exercises -- References -- Index.

This textbook on nonlinear optimization focuses on model building, real world problems, and applications of optimization models to natural and social sciences. Organized into two parts, this book may be used as a primary text for courses on convex optimization and non-convex optimization. Definitions, proofs, and numerical methods are well illustrated and all chapters contain compelling exercises. The exercises emphasize fundamental theoretical results on optimality and duality theorems, numerical methods with or without constraints, and derivative-free optimization. Selected solutions are given. Applications to theoretical results and numerical methods are highlighted to help students comprehend methods and techniques.

ISBN: 9783030111847

Standard No.: 10.1007/978-3-030-11184-7doiSubjects--Topical Terms:

517763
Mathematical optimization.

LC Class. No.: QA402.5

Dewey Class. No.: 519.6

Nonlinear optimization
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245 0 0 $a Nonlinear optimization $h [electronic resource] / $c by Francisco J. Aragon ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xiv, 350 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer undergraduate texts in mathematics and technology, $x 1867-5506
505 0 $a 1. Preliminaries -- Part I. Analytical Optimization -- 2. Convexity -- 3. Unconstrained Optimization -- 4. Convex Optimization -- Part II. Numerical Optimization -- 5. Unconstrained Optimization Algorithms -- 6. Constrained Optimization -- Solutions to selected exercises -- References -- Index.
520 $a This textbook on nonlinear optimization focuses on model building, real world problems, and applications of optimization models to natural and social sciences. Organized into two parts, this book may be used as a primary text for courses on convex optimization and non-convex optimization. Definitions, proofs, and numerical methods are well illustrated and all chapters contain compelling exercises. The exercises emphasize fundamental theoretical results on optimality and duality theorems, numerical methods with or without constraints, and derivative-free optimization. Selected solutions are given. Applications to theoretical results and numerical methods are highlighted to help students comprehend methods and techniques.
650 0 $a Mathematical optimization. $3 517763
650 0 $a Nonlinear theories. $3 524352
650 1 4 $a Optimization. $3 891104
700 1 $a Aragon, Francisco J. $3 3385651
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a Springer undergraduate texts in mathematics and technology. $3 1629118
856 4 0 $u https://doi.org/10.1007/978-3-030-11184-7
950 $a Mathematics and Statistics (Springer-11649)