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Fundamentals of convex analysis and ...

Correa, R.

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Fundamentals of convex analysis and optimization = a supremum function approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of convex analysis and optimization/ by Rafael Correa, Abderrahim Hantoute, Marco A. López.
Reminder of title:	a supremum function approach /
Author:	Correa, R.
other author:	Hantoute, Abderrahim.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xiii, 444 p. :ill., digital ;24 cm.
[NT 15003449]:	1. Introduction -- 2. Preliminaries -- 3. Fenchel-Moreau-Rockafellar theory -- 4. Fundamental topics in convex analysis -- 5. Supremum of convex functions -- 6. The supremum in specific contexts -- 7. Other subdifferential calculus rules -- 8. Miscellaneous -- 9. Exercises - Solutions -- Index -- Glossary of Notations -- Bibliography.
Contained By:	Springer Nature eBook
Subject:	Optimization. -
Online resource:	https://doi.org/10.1007/978-3-031-29551-5
ISBN:	9783031295515

Fundamentals of convex analysis and optimization = a supremum function approach /
Correa, R.

Fundamentals of convex analysis and optimizationa supremum function approach /[electronic resource] :by Rafael Correa, Abderrahim Hantoute, Marco A. López. - Cham :Springer International Publishing :2023. - xiii, 444 p. :ill., digital ;24 cm. - Springer series in operations research and financial engineering,2197-1773. - Springer series in operations research and financial engineering..

1. Introduction -- 2. Preliminaries -- 3. Fenchel-Moreau-Rockafellar theory -- 4. Fundamental topics in convex analysis -- 5. Supremum of convex functions -- 6. The supremum in specific contexts -- 7. Other subdifferential calculus rules -- 8. Miscellaneous -- 9. Exercises - Solutions -- Index -- Glossary of Notations -- Bibliography.

This book aims at an innovative approach within the framework of convex analysis and optimization, based on an in-depth study of the behavior and properties of the supremum of families of convex functions. It presents an original and systematic treatment of convex analysis, covering standard results and improved calculus rules in subdifferential analysis. The tools supplied in the text allow a direct approach to the mathematical foundations of convex optimization, in particular to optimality and duality theory. Other applications in the book concern convexification processes in optimization, non-convex integration of the Fenchel subdifferential, variational characterizations of convexity, and the study of Chebychev sets. At the same time, the underlying geometrical meaning of all the involved concepts and operations is highlighted and duly emphasized. A notable feature of the book is its unifying methodology, as well as the novelty of providing an alternative or complementary view to the traditional one in which the discipline is presented to students and researchers. This textbook can be used for courses on optimization, convex and variational analysis, addressed to graduate and post-graduate students of mathematics, and also students of economics and engineering. It is also oriented to provide specific background for courses on optimal control, data science, operations research, economics (game theory), etc. The book represents a challenging and motivating development for those experts in functional analysis, convex geometry, and any kind of researchers who may be interested in applications of their work.

ISBN: 9783031295515

Standard No.: 10.1007/978-3-031-29551-5doiSubjects--Topical Terms:

891104
Optimization.

LC Class. No.: QA331.5

Dewey Class. No.: 515.882

Fundamentals of convex analysis and optimization = a supremum function approach /
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245 1 0 $a Fundamentals of convex analysis and optimization $h [electronic resource] : $b a supremum function approach / $c by Rafael Correa, Abderrahim Hantoute, Marco A. López.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xiii, 444 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer series in operations research and financial engineering, $x 2197-1773
505 0 $a 1. Introduction -- 2. Preliminaries -- 3. Fenchel-Moreau-Rockafellar theory -- 4. Fundamental topics in convex analysis -- 5. Supremum of convex functions -- 6. The supremum in specific contexts -- 7. Other subdifferential calculus rules -- 8. Miscellaneous -- 9. Exercises - Solutions -- Index -- Glossary of Notations -- Bibliography.
520 $a This book aims at an innovative approach within the framework of convex analysis and optimization, based on an in-depth study of the behavior and properties of the supremum of families of convex functions. It presents an original and systematic treatment of convex analysis, covering standard results and improved calculus rules in subdifferential analysis. The tools supplied in the text allow a direct approach to the mathematical foundations of convex optimization, in particular to optimality and duality theory. Other applications in the book concern convexification processes in optimization, non-convex integration of the Fenchel subdifferential, variational characterizations of convexity, and the study of Chebychev sets. At the same time, the underlying geometrical meaning of all the involved concepts and operations is highlighted and duly emphasized. A notable feature of the book is its unifying methodology, as well as the novelty of providing an alternative or complementary view to the traditional one in which the discipline is presented to students and researchers. This textbook can be used for courses on optimization, convex and variational analysis, addressed to graduate and post-graduate students of mathematics, and also students of economics and engineering. It is also oriented to provide specific background for courses on optimal control, data science, operations research, economics (game theory), etc. The book represents a challenging and motivating development for those experts in functional analysis, convex geometry, and any kind of researchers who may be interested in applications of their work.
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700 1 $a Hantoute, Abderrahim. $3 3663789
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