Constrained Global Optimization: Algorithms and Applications

✍ Scribed by Panos M. Pardalos, J. Ben Rosen (eds.)

• Publisher: Springer-Verlag Berlin Heidelberg
• Year: 1987
• Tongue: English
• Leaves: 152
• Series: Lecture Notes in Computer Science 268
• Edition: 1
• Category: Library

✦ Synopsis

Global optimization is concerned with the characterization and computation of global minima or maxima of nonlinear functions. Such problems are widespread in mathematical modeling of real world systems for a very broad range of applications. The applications include economies of scale, fixed charges, allocation and location problems, quadratic assignment and a number of other combinatorial optimization problems. More recently it has been shown that certain aspects of VLSI chip design and database problems can be formulated as constrained global optimization problems with a quadratic objective function. Although standard nonlinear programming algorithms will usually obtain a local minimum to the problem , such a local minimum will only be global when certain conditions are satisfied (such as f and K being convex).

✦ Table of Contents

Convex sets and functions....Pages 1-12
Optimality conditions in nonlinear programming....Pages 13-23
Combinatorial optimization problems that can be formulated as nonconvex quadratic problems....Pages 24-33
Enumerative methods in nonconvex programming....Pages 34-48
Cutting plane methods....Pages 49-57
Branch and bound methods....Pages 58-74
Bilinear programming methods for nonconvex quadratic problems....Pages 75-83
Large scale problems....Pages 84-100
Global minimization of indefinite quadratic problems....Pages 101-117
Test problems for global nonconvex quadratic programming algorithms....Pages 118-122

✦ Subjects

Numerical Analysis