During the past twenty years, the linear complementarity problem has emerged as an important development in mathematical programming and numerical linear algebra. The Linear Complementarity Problem is a text designed to be suitable for both classroom use and as a references for researchers. The book is ideal for graduate students pursuing an advanced degree in operations research, but it is also of importance for many related fields of study, such as: computer science, applied mathematics, engineering, business studies, etc. * First comprehensive introductory text on the linear complementarity problem (LCP).* Involves all three major aspects on the LCP: theory, applications, and computation.* Text includes numerous exercises to illustrate the theory and computational procedures presented.

The Linear Complementarity Problem (Computer Science and Scientific Computing)

Business & Money

Pang, Jong-Shi

Cottle, Richard W.

Stone, Richard E.

Economics

Academic Press

Awarded the Frederick W. Lanchester Prize in 1994 for its valuable contributions to operations research and the management sciences, this mathematically rigorous book remains the standard reference on the linear complementarity problem. Its comprehensive treatment of the computation of equilibria arising from engineering, economics, and finance, plus chapter-ending exercises and 'Notes and References' sections make it equally useful for a graduate-level course or for self-study. For this new edition the authors have corrected typographical errors, revised difficult or faulty passages, and updated the bibliography.

The Linear Complementarity Problem (Classics in Applied Mathematics)

Society for Industrial and Applied Mathematics

This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization. As suggested by its title, the book is divided into two parts covering in their individual chapters LP Models and Applications; Linear Equations and Inequalities; The Simplex Algorithm; Simplex Algorithm Continued; Duality and the Dual Simplex Algorithm; Postoptimality Analyses; Computational Considerations; Nonlinear (NLP) Models and Applications; Unconstrained Optimization; Descent Methods; Optimality Conditions; Problems with Linear Constraints; Problems with Nonlinear Constraints; Interior-Point Methods; and an Appendix covering Mathematical Concepts. Each chapter ends with a set of exercises. 
The book is based on lecture notes the authors have used in numerous optimization courses the authors have taught at Stanford University. It emphasizes modeling and numerical algorithms for optimization with continuous (not integer) variables. The discussion presents the underlying theory without always focusing on formal mathematical proofs (which can be found in cited references). Another feature of this book is its inclusion of cultural and historical matters, most often appearing among the footnotes.
 "This book is a real gem. The authors do a masterful job of rigorously presenting all of the relevant theory clearly and concisely while managing to avoid unnecessary tedious mathematical details. This is an ideal book for teaching a one or two semester masters-level course in optimization – it broadly covers linear and nonlinear programming effectively balancing modeling, algorithmic theory, computation, implementation, illuminating historical facts, and numerous interesting examples and exercises. Due to the clarity of the exposition, this book also serves as a valuable reference for self-study." Professor Ilan Adler, IEOR Department, UC Berkeley "A carefully crafted introduction to the main elements and applications of mathematical optimization. This volume presents the essential concepts of linear and nonlinear programming in an accessible format filled with anecdotes, examples, and exercises that bring the topic to life. The authors plumb their decades of experience in optimization to provide an enriching layer of historical context. Suitable for advanced undergraduates and masters students in management science, operations research, and related fields."Michael P. Friedlander, IBM Professor of Computer Science, Professor of Mathematics, University of British Columbia

Linear and Nonlinear Optimization (International Series in Operations Research & Management Science, 253)

Thapa, Mukund N.

Management & Leadership

Springer

&#8203;This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization. As suggested by its title, the book is divided into two parts covering in their individual chapters LP Models and Applications; Linear Equations and Inequalities; The Simplex Algorithm; Simplex Algorithm Continued; Duality and the Dual Simplex Algorithm; Postoptimality Analyses; Computational Considerations; Nonlinear (NLP) Models and Applications; Unconstrained Optimization; Descent Methods; Optimality Conditions; Problems with Linear Constraints; Problems with Nonlinear Constraints; Interior-Point Methods; and an Appendix covering Mathematical Concepts. Each chapter ends with a set of exercises. The book is based on lecture notes the authors have used in numerous optimization courses the authors have taught at Stanford University.&nbsp; It emphasizes modeling and numerical algorithms for optimization with continuous (not integer) variables. The discussion presents the underlying theory without always focusing on formal mathematical proofs (which can be found in cited references). Another feature of this book is its inclusion of cultural and historical matters, most often appearing among the footnotes. <div> </div> <div>"This book is a real gem. The authors do a masterful job of rigorously presenting all of the relevant theory clearly and concisely while managing to avoid unnecessary tedious mathematical details.&nbsp; This is an ideal book for teaching a one or two semester masters-level course in optimization - it broadly covers linear and nonlinear programming effectively balancing modeling, algorithmic theory, computation, implementation,&nbsp;illuminating historical facts,&nbsp;and numerous interesting examples and exercises. Due to the clarity of the exposition, this book also serves as a valuable reference for self-study." </div> Professor Ilan Adler, IEOR Department, UC Berkeley <div> </div> <div>"A carefully crafted introduction to the main elements and applications of mathematical optimization. This volume presents the essential concepts of linear and nonlinear programming in an accessible format filled with anecdotes, examples, and exercises that bring the topic to life. The authors plumb their decades of experience in optimization to provide an enriching layer of historical context. Suitable for advanced undergraduates and masters students in management science, operations research, and related fields." Michael P. Friedlander, IBM Professor of Computer Science, Professor of Mathematics, University of British Columbia </div>

See the best price to sell, buy, or rent books by Cottle, Richard W.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Books by Cottle, Richard W.

See the best price to sell, buy, or rent books by Cottle, Richard W.. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

The Linear Complementarity Problem (Computer Science and Scientific Computing)

The Linear Complementarity Problem (Classics in Applied Mathematics)

Linear and Nonlinear Optimization (International Series in Operations Research & Management Science, 253)

Linear and Nonlinear Optimization (International Series in Operations Research & Management Science, 253)