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Image for Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming : Theory, Algorithms, Software, and Applications

Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming : Theory, Algorithms, Software, and Applications (2002 ed.)

By: Sahinidis, Nikolaos V. Tawarmalani, Mohit

Part of the Nonconvex Optimization and Its Applications series
1402010311 / 9781402010316
Hardback
519.76
31/10/2002
Stock expected by 04/04/2020
United States
155 x 235 mm, 1035 grams 478 pages, 89 Illustrations, black and white; XXV, 478 p. 89 illus.
Professional & Vocational  Learn More Postgraduate, Research & Scholarly Undergraduate

Interest in constrained optimization originated with the simple linear pro gramming model since it was practical and perhaps the only computationally tractable model at the time.

Constrained linear optimization models were soon adopted in numerous application areas and are perhaps the most widely used mathematical models in operations research and management science at the time of this writing.

Modelers have, however, found the assumption of linearity to be overly restrictive in expressing the real-world phenomena and problems in economics, finance, business, communication, engineering design, computational biology, and other areas that frequently demand the use of nonlinear expressions and discrete variables in optimization models.

Both of these extensions of the linear programming model are NP-hard, thus representing very challenging problems.

On the brighter side, recent advances in algorithmic and computing technology make it possible to re visit these problems with the hope of solving practically relevant problems in reasonable amounts of computational time.

Initial attempts at solving nonlinear programs concentrated on the de velopment of local optimization methods guaranteeing globality under the assumption of convexity.

On the other hand, the integer programming liter ature has concentrated on the development of methods that ensure global optima.

The aim of this book is to marry the advancements in solving nonlinear and integer programming models and to develop new results in the more general framework of mixed-integer nonlinear programs (MINLPs) with the goal of devising practically efficient global optimization algorithms for MINLPs.

BIC:

PBU Optimization, PBW Applied mathematics, UM Computer programming / software development

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