Useful as both a text and a reference, this book provides help to research analysts, applied mathematicians, economists, and statisticians. Gass (professor emeritus, business, University of Maryland)
This graduate textbook derives the theory of convex sets and functions, convex programming, nonlinear convex duality, and generalized convexity, then introduces numerical solution techniques for nonli
In the past three decades, local search has grown from a simple heuristic idea into a mature field of research in combinatorial optimization that is attracting ever-increasing attention. Local search
Introduction to sequential decision processes covers use of dynamic programming in studying models of resource allocation, methods for approximating solutions of control problems in continuous time, p
An introduction to the mathematical theory of multistage decision processes, this text takes a "functional equation" approach to the discovery of optimum policies. The text examines existence and uni
Bilevel programming problems are hierarchical optimization problems where the constraints of one problem (the so-called upper level problem) are defined in part by a second parametric optimization
Semidefinite programming (SDP) is one of the most exciting and active research areas in optimization. It has and continues to attract researchers with very diverse backgrounds, including experts in
This college textbook introduces algorithms for solving real problems that arise frequently in computer applications, basic principles of computational complexity, and NP -completeness and parallel a
The focus of this book is on mathematical programming which combines elements of hierarchical optimization and game theory. The basic model addresses the problem where two decision makers, each with
Sets out a new method for generating tight linear or convex programming relaxations for discrete and continuous nonconvex programming problems, featuring a model that affords a useful representation a
Optimization problems arising in practice usually contain several random parameters. Hence, in order to obtain optimal solutions being robust with respect to random parameter variations, the mostly av
Encompassing all the major topics students will encounter in courses on the subject, the authors teach both the underlying mathematical foundations and how these ideas are implemented in practice. The
This book summarizes developments related to a class of methods called Stochastic Decomposition (SD) algorithms, which represent an important shift in the design of optimization algorithms. Unlike tra
The new edition contains modifications and additions which take into account recent developments in the field. From the reviews of earlier editions: "The book is clearly written and lucidly structured
Stochastic Programming is the science that provides us with tools to design and control stochastic systems with the aid of mathematical programming techniques. It is on the border line of statistics a
Linear programming finds the least expensive way to meet given needs with available resources. Its results are used in every area of engineering and commerce: agriculture, oil refining, banking, and
In this book the rapidly developing field of Interior Point Methods (IPMs) is described.An extensive analysis of so-called path-following methods for linear programming, quadratic programming and conv
This text is concerned primarily with the theory of linear and nonlinear programming, and a number of closely-related problems, and with algorithms appropriate to those problems. In the first part of
