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Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming - Theory, Algorithms, Software, and Applications (Hardcover, 2002 ed.)
Loot Price: R5,819
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Convexification and Global Optimization in Continuous and Mixed-Integer Nonlinear Programming - Theory, Algorithms, Software, and Applications (Hardcover, 2002 ed.)
Series: Nonconvex Optimization and Its Applications, 65
Expected to ship within 10 - 15 working days
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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."
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