Introduction to Global Optimization (Nonconvex Optimization and its Applications)

Hardcover, second edition, xiv + 353 pages, NOT ex-library. A gentle crease in side margin of a portion of leaves; faint grubby handling marks on page edges externally. Interior is clean and bright throughout, with unmarked text, free of inscriptions and stamps, firmly bound. Boards show gentle handling wear, limited scuffing. Published without a dust jacket. -- 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. -- Contents: Fundamental Results; Convex Sets and Functions; General Properties of Optimization Problems; Convex Envelope; Kuhn-Tucker Conditions; Second Order Optimality Conditions; Duality in Nonlinear Programming; Complexity Issues; Quadratic Programming; Quadratic Integer Programming; Linear Complementarity Problem; Complexity of Quadratic Optimization; Enumerative Methods; Separation and Interpolation; General Concave Minimization; Applications; Basic Operations; Cutting Plane Algorithms; Outer Approximation Algorithms; Inner Approximation; Branch and Bound Algorithms; Simplicial Branch and Bound Approach; D.C. Programming; Space of D.C. Functions; Some Additional Applications; Optimality Conditions; Canonical D.C. Program; Simplicial Branch and Bound Algorithm; Prismatic Algorithm; Lipschitz Optimization; Lipschitz Functions; Lipschitz Optimization Problems; Lower Bounds; Branch and Bound Algorithms; Implementation; Global Optimization on Networks; Some MCCFP Models and its Complexity; Solution Methods; Decomposition Algorithms; Variable Decomposition: Conical Algorithm; Variable Decomposition: Outer Approximation; Constraint Decomposition: Conical Algorithm; Constraint Decomposition: Cutting Algorithm; Solutions; Selected References; Index.…