Linear Complementarity Linear And Nonlinear Programming PDF Download
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Author | : Katta G. Murty |
Publisher | : |
Total Pages | : 708 |
Release | : 1988 |
Genre | : Linear complementarity problem |
ISBN | : |
Download Linear Complementarity, Linear and Nonlinear Programming Book in PDF, ePub and Kindle
Author | : Richard W. Cottle |
Publisher | : SIAM |
Total Pages | : 781 |
Release | : 2009-08-27 |
Genre | : Mathematics |
ISBN | : 0898716861 |
Download The Linear Complementarity Problem Book in PDF, ePub and Kindle
A revised edition of the standard reference on the linear complementarity problem.
Author | : Katta G. Murty |
Publisher | : |
Total Pages | : 604 |
Release | : 1985 |
Genre | : Mathematics |
ISBN | : |
Download Linear and Combinatorial Programming Book in PDF, ePub and Kindle
Author | : Richard Cottle |
Publisher | : American Mathematical Soc. |
Total Pages | : 212 |
Release | : 1976 |
Genre | : Mathematics |
ISBN | : 9780821813294 |
Download Nonlinear Programming Book in PDF, ePub and Kindle
Author | : David G. Luenberger |
Publisher | : Springer Science & Business Media |
Total Pages | : 546 |
Release | : 2008-06-20 |
Genre | : Business & Economics |
ISBN | : 0387745033 |
Download Linear and Nonlinear Programming Book in PDF, ePub and Kindle
This third edition of the classic textbook in Optimization has been fully revised and updated. It comprehensively covers modern theoretical insights in this crucial computing area, and will be required reading for analysts and operations researchers in a variety of fields. The book connects the purely analytical character of an optimization problem, and the behavior of algorithms used to solve it. Now, the third edition has been completely updated with recent Optimization Methods. The book also has a new co-author, Yinyu Ye of California’s Stanford University, who has written lots of extra material including some on Interior Point Methods.
Author | : David G. Luenberger |
Publisher | : Springer Science & Business Media |
Total Pages | : 514 |
Release | : 2003-09-30 |
Genre | : Mathematics |
ISBN | : 9781402075933 |
Download Linear and Nonlinear Programming Book in PDF, ePub and Kindle
The original edition of this book was celebrated for its coverage of the central concepts of practical optimization techniques. This updated edition expands and illuminates the connection between the purely analytical character of an optimization problem, expressed by properties of the necessary conditions, and the behavior of algorithms used to solve a problem. Incorporating modern theoretical insights, this classic text is even more useful.
Author | : Evar D. Nering |
Publisher | : Academic Press |
Total Pages | : 618 |
Release | : 1993 |
Genre | : Computers |
ISBN | : 9780125154406 |
Download Linear Programs and Related Problems Book in PDF, ePub and Kindle
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 the book, the authors introduce the concept of duality which serves as a unifying concept throughout the book. The simplex algorithm is presented along with modifications and adaptations to problems with special structures. Two alternative algorithms, the ellipsoidal algorithm and Karmarker's algorithm, are also discussed, along with numerical considerations. the second part of the book looks at specific types of problems and methods for their solution. This book is designed as a textbook for mathematical programming courses, and each chapter contains numerous exercises and examples.
Author | : Richard W. Cottle |
Publisher | : Springer |
Total Pages | : 644 |
Release | : 2017-06-11 |
Genre | : Business & Economics |
ISBN | : 1493970550 |
Download Linear and Nonlinear Optimization Book in PDF, ePub and Kindle
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
Author | : Mokhtar S. Bazaraa |
Publisher | : John Wiley & Sons |
Total Pages | : 867 |
Release | : 2013-06-12 |
Genre | : Mathematics |
ISBN | : 1118626303 |
Download Nonlinear Programming Book in PDF, ePub and Kindle
COMPREHENSIVE COVERAGE OF NONLINEAR PROGRAMMING THEORY AND ALGORITHMS, THOROUGHLY REVISED AND EXPANDED Nonlinear Programming: Theory and Algorithms—now in an extensively updated Third Edition—addresses the problem of optimizing an objective function in the presence of equality and inequality constraints. Many realistic problems cannot be adequately represented as a linear program owing to the nature of the nonlinearity of the objective function and/or the nonlinearity of any constraints. The Third Edition begins with a general introduction to nonlinear programming with illustrative examples and guidelines for model construction. Concentration on the three major parts of nonlinear programming is provided: Convex analysis with discussion of topological properties of convex sets, separation and support of convex sets, polyhedral sets, extreme points and extreme directions of polyhedral sets, and linear programming Optimality conditions and duality with coverage of the nature, interpretation, and value of the classical Fritz John (FJ) and the Karush-Kuhn-Tucker (KKT) optimality conditions; the interrelationships between various proposed constraint qualifications; and Lagrangian duality and saddle point optimality conditions Algorithms and their convergence, with a presentation of algorithms for solving both unconstrained and constrained nonlinear programming problems Important features of the Third Edition include: New topics such as second interior point methods, nonconvex optimization, nondifferentiable optimization, and more Updated discussion and new applications in each chapter Detailed numerical examples and graphical illustrations Essential coverage of modeling and formulating nonlinear programs Simple numerical problems Advanced theoretical exercises The book is a solid reference for professionals as well as a useful text for students in the fields of operations research, management science, industrial engineering, applied mathematics, and also in engineering disciplines that deal with analytical optimization techniques. The logical and self-contained format uniquely covers nonlinear programming techniques with a great depth of information and an abundance of valuable examples and illustrations that showcase the most current advances in nonlinear problems.
Author | : Masakazu Kojima |
Publisher | : Springer Science & Business Media |
Total Pages | : 124 |
Release | : 1991-09-25 |
Genre | : Language Arts & Disciplines |
ISBN | : 9783540545095 |
Download A Unified Approach to Interior Point Algorithms for Linear Complementarity Problems Book in PDF, ePub and Kindle
Following Karmarkar's 1984 linear programming algorithm, numerous interior-point algorithms have been proposed for various mathematical programming problems such as linear programming, convex quadratic programming and convex programming in general. This monograph presents a study of interior-point algorithms for the linear complementarity problem (LCP) which is known as a mathematical model for primal-dual pairs of linear programs and convex quadratic programs. A large family of potential reduction algorithms is presented in a unified way for the class of LCPs where the underlying matrix has nonnegative principal minors (P0-matrix). This class includes various important subclasses such as positive semi-definite matrices, P-matrices, P*-matrices introduced in this monograph, and column sufficient matrices. The family contains not only the usual potential reduction algorithms but also path following algorithms and a damped Newton method for the LCP. The main topics are global convergence, global linear convergence, and the polynomial-time convergence of potential reduction algorithms included in the family.