Mathematics Of Optimization Smooth And Nonsmooth Case

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Author: Giorgio Giorgi
Publisher: Elsevier
ISBN: 9780080535951
Size: 75.62 MB
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Mathematics Of Optimization Smooth And Nonsmooth Case by Giorgio Giorgi


Original Title: Mathematics Of Optimization Smooth And Nonsmooth Case

The book is intended for people (graduates, researchers, but also undergraduates with a good mathematical background) involved in the study of (static) optimization problems (in finite-dimensional spaces). It contains a lot of material, from basic tools of convex analysis to optimality conditions for smooth optimization problems, for non smooth optimization problems and for vector optimization problems. The development of the subjects are self-contained and the bibliographical references are usually treated in different books (only a few books on optimization theory deal also with vector problems), so the book can be a starting point for further readings in a more specialized literature. Assuming only a good (even if not advanced) knowledge of mathematical analysis and linear algebra, this book presents various aspects of the mathematical theory in optimization problems. The treatment is performed in finite-dimensional spaces and with no regard to algorithmic questions. After two chapters concerning, respectively, introductory subjects and basic tools and concepts of convex analysis, the book treats extensively mathematical programming problems in the smmoth case, in the nonsmooth case and finally vector optimization problems. · Self-contained · Clear style and results are either proved or stated precisely with adequate references · The authors have several years experience in this field · Several subjects (some of them non usual in books of this kind) in one single book, including nonsmooth optimization and vector optimization problems · Useful long references list at the end of each chapter

Invexity And Optimization

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Author: Shashi K. Mishra
Publisher: Springer Science & Business Media
ISBN: 3540785612
Size: 78.71 MB
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Invexity And Optimization by Shashi K. Mishra


Original Title: Invexity And Optimization

Invexity and Optimization presents results on invex function and their properties in smooth and nonsmooth cases, pseudolinearity and eta-pseudolinearity. Results on optimality and duality for a nonlinear scalar programming problem are presented, second and higher order duality results are given for a nonlinear scalar programming problem, and saddle point results are also presented. Invexity in multiobjective programming problems and Kuhn-Tucker optimality conditions are given for a multiobjecive programming problem, Wolfe and Mond-Weir type dual models are given for a multiobjective programming problem and usual duality results are presented in presence of invex functions. Continuous-time multiobjective problems are also discussed. Quadratic and fractional programming problems are given for invex functions. Symmetric duality results are also given for scalar and vector cases.

Constructive Nonsmooth Analysis And Related Topics

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Author: Vladimir F. Demyanov
Publisher: Springer Science & Business Media
ISBN: 1461486157
Size: 42.63 MB
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Constructive Nonsmooth Analysis And Related Topics by Vladimir F. Demyanov


Original Title: Constructive Nonsmooth Analysis And Related Topics

This volume contains a collection of papers based on lectures and presentations delivered at the International Conference on Constructive Nonsmooth Analysis (CNSA) held in St. Petersburg (Russia) from June 18-23, 2012. This conference was organized to mark the 50th anniversary of the birth of nonsmooth analysis and nondifferentiable optimization and was dedicated to J.-J. Moreau and the late B.N. Pshenichnyi, A.M. Rubinov, and N.Z. Shor, whose contributions to NSA and NDO remain invaluable. The first four chapters of the book are devoted to the theory of nonsmooth analysis. Chapters 5-8 contain new results in nonsmooth mechanics and calculus of variations. Chapters 9-13 are related to nondifferentiable optimization, and the volume concludes with four chapters containing interesting and important historical chapters, including tributes to three giants of nonsmooth analysis, convexity, and optimization: Alexandr Alexandrov, Leonid Kantorovich, and Alex Rubinov. The last chapter provides an overview and important snapshots of the 50-year history of convex analysis and optimization.

Generalized Convexity Nonsmooth Variational Inequalities And Nonsmooth Optimization

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Author: Qamrul Hasan Ansari
Publisher: CRC Press
ISBN: 1439868212
Size: 44.35 MB
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Generalized Convexity Nonsmooth Variational Inequalities And Nonsmooth Optimization by Qamrul Hasan Ansari


Original Title: Generalized Convexity Nonsmooth Variational Inequalities And Nonsmooth Optimization

Until now, no book addressed convexity, monotonicity, and variational inequalities together. Generalized Convexity, Nonsmooth Variational Inequalities, and Nonsmooth Optimization covers all three topics, including new variational inequality problems defined by a bifunction. The first part of the book focuses on generalized convexity and generalized monotonicity. The authors investigate convexity and generalized convexity for both the differentiable and nondifferentiable case. For the nondifferentiable case, they introduce the concepts in terms of a bifunction and the Clarke subdifferential. The second part offers insight into variational inequalities and optimization problems in smooth as well as nonsmooth settings. The book discusses existence and uniqueness criteria for a variational inequality, the gap function associated with it, and numerical methods to solve it. It also examines characterizations of a solution set of an optimization problem and explores variational inequalities defined by a bifunction and set-valued version given in terms of the Clarke subdifferential. Integrating results on convexity, monotonicity, and variational inequalities into one unified source, this book deepens your understanding of various classes of problems, such as systems of nonlinear equations, optimization problems, complementarity problems, and fixed-point problems. The book shows how variational inequality theory not only serves as a tool for formulating a variety of equilibrium problems, but also provides algorithms for computational purposes.

Generalized Convexity Generalized Monotonicity Optimality Conditions And Duality In Scaler And Vector Optimization

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Author: Alberto Cambini
Publisher:
ISBN: 9788190149310
Size: 35.44 MB
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Generalized Convexity Generalized Monotonicity Optimality Conditions And Duality In Scaler And Vector Optimization by Alberto Cambini


Original Title: Generalized Convexity Generalized Monotonicity Optimality Conditions And Duality In Scaler And Vector Optimization

The aim of this volume is to strengthen the interest in generalized convexity, generalized monotonicity and related areas and to stimulate new research in these fields by update survey (or recent results) of known experts covering many important topics such as some new theoretical aspects of generalized convexity and generalized invexity, some applications of generalized monotonicity and pseudomonotonicity to equilibrium problems and to economic and financial problems, some applications of abstract convexity, some applications of discrete convex analysis to cooperative game theory, fractional programming, optimality conditions in vector optimization (smooth and non-smooth), semi-infinite optimization and a new method for solving multiobjective problems.

Nondifferentiable And Two Level Mathematical Programming

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Author: Kiyotaka Shimizu
Publisher: Springer Science & Business Media
ISBN: 1461563054
Size: 44.32 MB
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Nondifferentiable And Two Level Mathematical Programming by Kiyotaka Shimizu


Original Title: Nondifferentiable And Two Level Mathematical Programming

The analysis and design of engineering and industrial systems has come to rely heavily on the use of optimization techniques. The theory developed over the last 40 years, coupled with an increasing number of powerful computational procedures, has made it possible to routinely solve problems arising in such diverse fields as aircraft design, material flow, curve fitting, capital expansion, and oil refining just to name a few. Mathematical programming plays a central role in each of these areas and can be considered the primary tool for systems optimization. Limits have been placed on the types of problems that can be solved, though, by the difficulty of handling functions that are not everywhere differentiable. To deal with real applications, it is often necessary to be able to optimize functions that while continuous are not differentiable in the classical sense. As the title of the book indicates, our chief concern is with (i) nondifferentiable mathematical programs, and (ii) two-level optimization problems. In the first half of the book, we study basic theory for general smooth and nonsmooth functions of many variables. After providing some background, we extend traditional (differentiable) nonlinear programming to the nondifferentiable case. The term used for the resultant problem is nondifferentiable mathematical programming. The major focus is on the derivation of optimality conditions for general nondifferentiable nonlinear programs. We introduce the concept of the generalized gradient and derive Kuhn-Tucker-type optimality conditions for the corresponding formulations.

Generalized Convexity Generalized Monotonicity Recent Results

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Author: Jean-Pierre Crouzeix
Publisher: Springer Science & Business Media
ISBN: 9780792350880
Size: 61.75 MB
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Generalized Convexity Generalized Monotonicity Recent Results by Jean-Pierre Crouzeix


Original Title: Generalized Convexity Generalized Monotonicity Recent Results

A function is convex if its epigraph is convex. This geometrical structure has very strong implications in terms of continuity and differentiability. Separation theorems lead to optimality conditions and duality for convex problems. A function is quasiconvex if its lower level sets are convex. Here again, the geo metrical structure of the level sets implies some continuity and differentiability properties for quasiconvex functions. Optimality conditions and duality can be derived for optimization problems involving such functions as well. Over a period of about fifty years, quasiconvex and other generalized convex functions have been considered in a variety of fields including economies, man agement science, engineering, probability and applied sciences in accordance with the need of particular applications. During the last twenty-five years, an increase of research activities in this field has been witnessed. More recently generalized monotonicity of maps has been studied. It relates to generalized convexity off unctions as monotonicity relates to convexity. Generalized monotonicity plays a role in variational inequality problems, complementarity problems and more generally, in equilibrium prob lems.

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