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Introduction to Global Optimization - Second Edition (Nonconvex Optimization and its Applications, Volume 48)
 
 
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Introduction to Global Optimization - Second Edition (Nonconvex Optimization and its Applications, Volume 48) [Hardcover]

R. Horst (Author), Panos M. Pardalos (Author), Nguyen Van Thoai (Author)
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Book Description

0792365747 978-0792365747 October 31, 2000 2nd
Most of the existing books on optimization focus on the problem of computing locally optimal solutions. Global optimization is concerned with the computation and characterization of global optima of nonlinear functions. Global optimization problems are widespread in the mathematical modeling of real world systems for a very broad range of applications. During the past three decades many new theoretical, algorithmic, and computational contributions have helped to solve globally multi-extreme problems arising from important practical applications. Introduction to Global Optimization is the first comprehensive textbook that covers the fundamentals in global optimization. The second edition includes algorithms, applications, and complexity results for quadratic programming, concave minimization, DC and Lipshitz problems, decomposition algorithms for nonconvex optimization, and nonlinear network flow problems. Each chapter contains illustrative examples and ends with carefully selected exercises, which are designed to help the student to get a grasp of the material and enhance their knowledge of global optimization methods. Audience: This textbook is addressed not only to students of mathematical programming, but to all scientists in various disciplines who need global optimization methods to model and solve problems.

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Editorial Reviews

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Comments on the first edition: `Each chapter contains illlustrative examples and exercises. This excellent book is the first textbook on deterministic global optimization.' Mathematical Reviews (96g:90001) `The authors provide a nice selection of homework exercises (with solutions) at an appropriate level and a good mixture of theory, application, and numerical problems throughout the text. The text would be perfect for a course on global optimization.' Interfaces 28 (1998) `Overall, this book provides an excellent introduction to the fascinating field of global optimization. The authors have used their extensive knowledge of and perspective on the field to create a coherent text that is accessible to a large audience that includes both students of mathematical programming and scientists who utilize optimization in their work.' Journal of Global Optimization 9 (1996)

Product Details

  • Hardcover: 353 pages
  • Publisher: Springer; 2nd edition (October 31, 2000)
  • Language: English
  • ISBN-10: 0792365747
  • ISBN-13: 978-0792365747
  • Product Dimensions: 9.3 x 6.5 x 1.1 inches
  • Shipping Weight: 1.3 pounds (View shipping rates and policies)
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Best Sellers Rank: #5,404,377 in Books (See Top 100 in Books)

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2 of 3 people found the following review helpful:
5.0 out of 5 stars excelent textbok, September 30, 2007
the authors are very famous in the field. the textbook is well organized and is very frontior.
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Inside This Book (learn more)
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First Sentence:
Geometric concepts play a dominant role in the development and analysis of algorithms for solving optimization problems. Read the first page
Key Phrases - Statistically Improbable Phrases (SIPs): (learn more)
standard polytope, design centering problem, best feasible point, simplicial branch, concavity cut, vertex enumeration problem, radial subdivision, concave minimization problem, conical partition, nondegenerate vertex, exhaustive subdivision, conical branch, concave programming problem, best objective function value, maximum clique problem, convex envelope, global minimum point, feasible domain, forcing rules, linear program min, optimal objective function value, network flow problem, computing lower bounds, quadratic problem, local minimizer
Key Phrases - Capitalized Phrases (CAPs): (learn more)
Convergence Proposition, Prove Theorem
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