Strict Convexity and Complex Strict Convexity
Strict Convexity and Complex Strict Convexity

Author: Istratescu

Publisher: Routledge

Published: 2017-10-19

Total Pages: 332

ISBN-13: 1351413325

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This important work provides a comprehensive overview of the properties of Banachspaces related to strict convexity and a survey of significant applications-uniting a wealthof information previously scattered throughout the mathematical literature in a well-organized,accessible format.After introducing the subject through a discussion of the basic results of linear functionalanalysis, this unique book proceeds to investigate the characteristics of strictly convexspaces and related classes, including uniformly convex spaces, and examine important applicationsregarding approximation theory and fixed point theory. Following this extensivetreatment, the book discusses complex strictly convex spaces and related spaces- alsowith applications. Complete, clearly elucidated proofs accompany results throughout thebook, and ample references are provided to aid further research of the subject.Strict Convexity and Complex Strict Convexity is essential fot mathematicians and studentsinterested in geometric theory of Banach spaces and applications to approximationtheory and fixed point theory, and is of great value to engineers working in optimizationstudies. In addition, this volume serves as an excellent text for a graduate course inGeometric Theory of Banach Spaces.

Convex Optimization
Convex Optimization

Author: Stephen P. Boyd

Publisher: Cambridge University Press

Published: 2004-03-08

Total Pages: 744

ISBN-13: 9780521833783

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Convex optimization problems arise frequently in many different fields. This book provides a comprehensive introduction to the subject, and shows in detail how such problems can be solved numerically with great efficiency. The book begins with the basic elements of convex sets and functions, and then describes various classes of convex optimization problems. Duality and approximation techniques are then covered, as are statistical estimation techniques. Various geometrical problems are then presented, and there is detailed discussion of unconstrained and constrained minimization problems, and interior-point methods. The focus of the book is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. It contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance and economics.

Polynomial Convexity
Polynomial Convexity

Author: Edgar Lee Stout

Publisher: Springer Science & Business Media

Published: 2007-05-03

Total Pages: 454

ISBN-13: 0817645373

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This comprehensive monograph details polynomially convex sets. It presents the general properties of polynomially convex sets with particular attention to the theory of the hulls of one-dimensional sets. Coverage examines in considerable detail questions of uniform approximation for the most part on compact sets but with some attention to questions of global approximation on noncompact sets. The book also discusses important applications and motivates the reader with numerous examples and counterexamples, which serve to illustrate the general theory and to delineate its boundaries.

Optimization of Computer Networks
Optimization of Computer Networks

Author: Pablo Pavón Mariño

Publisher: John Wiley & Sons

Published: 2016-05-02

Total Pages: 399

ISBN-13: 1119013356

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This book covers the design and optimization of computer networks applying a rigorous optimization methodology, applicable to any network technology. It is organized into two parts. In Part 1 the reader will learn how to model network problems appearing in computer networks as optimization programs, and use optimization theory to give insights on them. Four problem types are addressed systematically – traffic routing, capacity dimensioning, congestion control and topology design. Part 2 targets the design of algorithms that solve network problems like the ones modeled in Part 1. Two main approaches are addressed – gradient-like algorithms inspiring distributed network protocols that dynamically adapt to the network, or cross-layer schemes that coordinate the cooperation among protocols; and those focusing on the design of heuristic algorithms for long term static network design and planning problems. Following a hands-on approach, the reader will have access to a large set of examples in real-life technologies like IP, wireless and optical networks. Implementations of models and algorithms will be available in the open-source Net2Plan tool from which the user will be able to see how the lessons learned take real form in algorithms, and reuse or execute them to obtain numerical solutions. An accompanying link to the author’s own Net2plan software enables readers to produce numerical solutions to a multitude of real-life problems in computer networks (www.net2plan.com).