: Linear and Nonlinear Optimization () by Igor Griva; Stephen G. Nash; Ariela Sofer and a great selection of similar New, Used. Provides an introduction to the applications, theory, and algorithms of linear and nonlinear optimization. The emphasis is on practical aspects. by Igor Griva, Stephen G. Nash, Ariela Sofer This book is primarily intended for use in linear and nonlinear optimization courses for advanced undergraduate.

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Linear and Nonlinear Optimization

Computational complexity of linear programming; The book includes several examples of realistic optimization models that address important applications. Geometry of linear programming; 5.

His research activities are centered in scientific computing, especially nonlinear optimization, along with related interests in statistical computing and optimal control. Contents Preface Index This book introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material.

Part I of Linear and Nonlinear Optimization, Second Edition provides fundamentals that can be taught in whole or in part at the beginning of a course on either topic and then referred to as needed. We’re featuring millions of their reader ratings on our book pages to help you find your new favourite book. Low-storage methods for unconstrained problems; Part IV.

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His research focuses on theory and methods of nonlinear optimization and their application to problems in grica and engineering. Click the button below to add the Linear and Ihor Optimization, Second Edition to your wish list.

Enabling JavaScript in your browser will allow you to experience all the features of our site. Table of contents Preface; Part I. Basics of unconstrained optimization; Iterative Methods for Linear Systems: Fundamentals of optimization; 3. Home Contact Us Help Free delivery worldwide.

NashAriela Sofer Limited preview – In the preface the authors suggest course outlines that can be adjusted to the requirements of a particular course on both linear and nonlinear optimization, or to separate courses on these topics.

The book is designed to be flexible.

Linear and Nonlinear Optimization, Second Edition

Checking availability for Buy Online, Pick up in Oprimization The book is designed to limear flexible. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support vector machines.

Customer Reviews Average Review. See All Customer Reviews. The simplex method; 6. Goodreads is the world’s largest site for readers with over 50 million reviews.

Account Options Sign in. Buy in bulk and save. Penalty and barrier methods; Part Anv. Her major areas of interest are nonlinear optimization, and optimization in biomedical applications. Duality and sensitivity; 7.

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Linear and Nonlinear Optimization

Penalty and barrier methods; Part V. We promise to never spam you, and just use your email address to identify you as a valid customer. Other fundamentals; Itor C. His research focuses on theory and methods of nonlinear optimization and their application to problems in science and engineering.

His research focuses on the theory and methods of nonlinear optimization and their application to problems in science and engineering. Enhancements of the simplex method; 8. Product Reviews Write review. The emphasis is on practical aspects – discussing modern algorithms, as well as the influence of theory on the interpretation of solutions or on the design of software. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support-vector machines.