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Showing posts with label Optimization. Show all posts
Showing posts with label Optimization. Show all posts

Handbook of Generalized Convexity and Generalized Monotonicity by Nicolas Hadjisavvas

Posted by Mr Dragon at Wednesday, November 23, 2011 , 0 Comments

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Book Details :
Title : Handbook of Generalized Convexity and Generalized Monotonicity (Nonconvex Optimization and Its Applications)
Author : Nicolas Hadjisavvas, Sándor Komlósi, Siegfried S. Schaible
Hardcover : 692 pages
Publisher : Springer; 1 edition (October 20, 2004)
Language : English
ISBN-10 : 0387232559
ISBN-13 : 978-0387232553
Size : 11.33 MB.
Type : Pdf.
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Book Description :

Studies in generalized convexity and generalized monotonicity have significantly increased during the last two decades. Researchers with very diverse backgrounds such as mathematical programming, optimization theory, convex analysis, nonlinear analysis, nonsmooth analysis, linear algebra, probability theory, variational inequalities, game theory, economic theory, engineering, management science, equilibrium analysis, for example are attracted to this fast growing field of study. Such enormous research activity is partially due to the discovery of a rich, elegant and deep theory which provides a basis for interesting existing and potential applications in different disciplines. The handbook offers an advanced and broad overview of the current state of the field. It contains fourteen chapters written by the leading experts on the respective subject; eight on generalized convexity and the remaining six on generalized monotonicity.

Various generalizations of the classical concept of a convex function have been introduced, especially during the second half of the 20th century. Generalized convex functions are the many nonconvex functions which share at least one of the valuable properties of convex functions. Apart from their theoretical interest, they are often more suitable than convex functions to describe real-word problems in disciplines such as economics, engineering, management science, probability theory and in other applied sciences. More recently, generalized monotone maps which are closely related to generalized convex functions have also been studied extensively. While initial efforts to generalize convexity and monotonicity were limited to only a few research centers, today there are numerous researchers throughout the world and in various disciplines engaged in theoretical and applied studies of generalized convexity/monotonicity (see http://www.genconv.org).

The Handbook offers a systematic and thorough exposition of the theory and applications of the various aspects of generalized convexity and generalized monotonicity. It is aimed at the non-expert, for whom it provides a detailed introduction, as well as at the expert who seeks to learn about the latest developments and references in his research area. Results in this fast growing field are contained in a large number of scientific papers which appeared in a variety of professional journals, partially due to the interdisciplinary nature of the subject matter. Each of its fourteen chapters is written by leading experts of the respective research area starting from the very basics and moving on to the state of the art of the subject.

Each chapter is complemented by a comprehensive bibliography which will assist the non-expert and expert alike.


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Numerical Linear Algebra and Optimization Vol 1 by Philip E. Gill

Posted by Mr Dragon at Tuesday, November 15, 2011 , 0 Comments

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Book Details :
Title : Numerical Linear Algebra and Optimization, Vol. 1
Author : Philip E. Gill, Walter Murray, Margaret H. Wright
Hardcover : 448 pages
Publisher : Perseus Books (Sd) (September 1990)
Language : English
ISBN-10 : 0201126494
ISBN-13 : 978-0201126495
Size : 4.65 MB.
Type : Pdf.
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Book Description :

Our intent in writing this book is to provide a unified introduction for students and practitioners to three distinct but closely related areas:
1. Fundamentals of numerical analysis and scientific computing;
2. Techniques for solving linear systems and linear least-squares problems, and for updating matrix factorizations;
3. Numerical optimization methods for both linear and nonlinear programming.

These topics not only form a coherent body of knowledge for pedagogical purposes, but are also important background for anyone who wishes to understand and develop numerical methods and software. Unfortunately, they are rarely taught together. Introductory treatments of numerical analysis and scientific computing often do not mention optimization at all, or else refer to an extremely limited set of problem categories and methods.

Textbooks on linear algebra typically do not discuss techniques for updating matrix factorizations, which have become an essential part of modern linear algebra and optimization during the last 20 years. Finally, many books on optimization, particularly those on linear programming, include little if any discussion of numerical analysis.

In this book, a self-contained and consistent presentation is given of the three areas listed above. The level of difficulty is designed for those with basic knowledge of multivariate calculus and linear algebra, although most of the relevant background is summarized in the text. Some elementary knowledge of computer science is helpful in understanding the "pseudo-code" form of algorithms. The book is likely to be most useful for advanced undergraduate or masters-level courses, but could also be used to teach selected material for students at the Ph.D. level.

The book was started in 1987 to accompany a new course on "Scientific Computing and Numerical Optimization" in the Department of Operations Research at Stanford University. In the intervening years, various versions of the book have been used by the authors to teach both undergraduate and graduate courses at Stanford University, the University of California at San Diego, and Princeton University.

The progression of topics in Volumes 1 and 2 is designed to correspond to an intensive one-year course in numerical linear algebra and optimization, starting with linear algebra and working through nonlinearly constrained optimization. The topics also fit into other standard course sequences.

Volume 1 contains more than 200 exercises with a wide range of difficulty. Since students learn about the non-obvious complexities of computation only from their own experience, we have formulated a substantial number of the homework problems so that they may be solved with an interactive package for matrix calculations. Several such packages are already widely available—in particular, MATLAB™and Mathematica™. The ability to perform nontrivial calculations expressed in a natural form creates understanding of what "really happens" in numerical computation. (Without such a package, numerical exercises tend to be designed mainly to make the arithmetic simple rather than to present the relevant concepts.)

At the end of each chapter, we suggest further reading intended to provide more detailed coverage of the relevant topics. To avoid the impossible task of compiling even a marginally complete list of research papers, we have chosen to cite only books, with the occasional exception of research papers that have directly influenced our presentation.


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Foundations of Optimization by Osman Güler

Posted by Mr Dragon at Friday, October 21, 2011 , 0 Comments

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Book Details :
Title : Foundations of Optimization (Graduate Texts in Mathematics)
Author : Osman Güler
Hardcover : 457 pages
Publisher : Springer; 1st Edition. edition (August 25, 2010)
Language : English
ISBN-10 : 0387344314
ISBN-13 : 978-0387344317
Size : 2.3 MB.
Type : Pdf.
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Book Description :

The book gives a detailed and rigorous treatment of the theory of optimization (unconstrained optimization, nonlinear programming, semi-infinite programming, etc.) in finite-dimensional spaces. The fundamental results of convexity theory and the theory of duality in nonlinear programming and the theories of linear inequalities, convex polyhedra, and linear programming are covered in detail. Over two hundred, carefully selected exercises should help the students master the material of the book and give further insight. Some of the most basic results are proved in several independent ways in order to give flexibility to the instructor. A separate chapter gives extensive treatments of three of the most basic optimization algorithms (the steepest-descent method, Newton’s method, the conjugate-gradient method). The first chapter of the book introduces the necessary differential calculus tools used in the book. Several chapters contain more advanced topics in optimization such as Ekeland’s epsilon-variational principle, a deep and detailed study of separation properties of two or more convex sets in general vector spaces, Helly’s theorem and its applications to optimization, etc. The book is suitable as a textbook for a first or second course in optimization at the graduate level. It is also suitable for self-study or as a reference book for advanced readers. The book grew out of author’s experience in teaching a graduate level one-semester course a dozen times since 1993. Osman Guler is a Professor in the Department of Mathematics and Statistics at University of Maryland, Baltimore County. His research interests include mathematical programming, convex analysis, complexity of optimization problems, and operations research.

This book is an advanced graduate level text on the mathematical theory of optimization. … filled with many useful examples and counterexamples that provide intuition to support the more formal theorems and proofs. … There are also numerous exercises for the student. … The book will also be useful as a reference for researchers working in various areas of optimization.


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Stochastic Optimization Methods by Kurt Marti

Posted by Mr Dragon at Thursday, September 29, 2011 , 0 Comments

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Book Details :
Title : Stochastic Optimization Methods
Author : Kurt Marti
Hardcover : 353 pages
Publisher : Springer; 2nd ed. edition (June 5, 2008)
Language : English
ISBN-10 : 3540794573
ISBN-13 : 978-3540794578
Size : 10.88 MB.
Type : Pdf.
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Book Description :

Optimization problems arising in practice involve random model parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insenistive with respect to random parameter variations, appropriate deterministic substitute problems are needed. Based on the probability distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into appropriate deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Several deterministic and stochastic approximation methods are provided: Taylor expansion methods, regression and response surface methods (RSM), probability inequalities, multiple linearization of survival/failure domains, discretization methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation and gradient procedures, differentiation formulas for probabilities and expectations.

The book basically goes through the control problem under stochastic uncertainity, which is drawn from the application of engineering and operational research problems. … The most important feature of this book is that it has a collection of solution techniques used in optimization methods. … More of these applications on different disciplines such as economics … made the book accessible for a wider audience and led to a generally more interesting book.

The aim of the present book is to provide analytical and numerical tools, together with their mathematical foundations, for the approximate computation of robust optimal decisions/designs as needed in concrete engineering/economic applications. … The book is well written and the presentation is rigourous and self-contained.

About author :

Dr. Kurt Marti is a full Professor of Engineering Mathematics at the „Federal Armed Forces University of Munich“. He is Chairman of the IFIP-Working Group 7.7 on “Stochastic Optimization” and has been Chairman of the GAMM-Special Interest Group “Applied Stochastics and Optimization”. Professor Marti has published several books, both in German and in English, and he is author of more than 160 papers in refereed journals.


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