
Linear and Nonlinear Optimization
Society for Industrial & Applied Mathematics,U.S. (Publisher)
2nd Edition
Published on 30. December 2008
Book
Hardback
764 pages
978-0-89871-661-0 (ISBN)
Description
Introduces the applications, theory, and algorithms of linear and nonlinear optimization, with an emphasis on the practical aspects of the material. Its unique modular structure provides flexibility to accommodate the varying needs of instructors, students, and practitioners with different levels of sophistication in these topics. The succinct style of this second edition is punctuated with numerous real-life examples and exercises, and the authors include accessible explanations of topics that are not often mentioned in textbooks, such as duality in nonlinear optimization, primal-dual methods for nonlinear optimization, filter methods, and applications such as support-vector machines.
Part I provides fundamentals that can be taught in whole or in part at the beginning of a course on either topic and then referred to as needed. Part II on linear programming and Part III on unconstrained optimization can be used together or separately, and Part IV on nonlinear optimization can be taught without having studied the material in Part II. In the preface the authors suggest course outlines that can be adjusted to the requirements of a particular course on both linear and nonlinear optimization, or to separate courses on these topics. Three appendices provide information on linear algebra, other fundamentals, and software packages for optimization problems. A supplemental website offers auxiliary data sets that are necessary for some of the exercises.
Part I provides fundamentals that can be taught in whole or in part at the beginning of a course on either topic and then referred to as needed. Part II on linear programming and Part III on unconstrained optimization can be used together or separately, and Part IV on nonlinear optimization can be taught without having studied the material in Part II. In the preface the authors suggest course outlines that can be adjusted to the requirements of a particular course on both linear and nonlinear optimization, or to separate courses on these topics. Three appendices provide information on linear algebra, other fundamentals, and software packages for optimization problems. A supplemental website offers auxiliary data sets that are necessary for some of the exercises.
More details
Edition
Second Edition
Language
English
Place of publication
New York
United States
Target group
College/higher education
Edition type
New edition
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 264 mm
Width: 187 mm
Thickness: 43 mm
Weight
1475 gr
ISBN-13
978-0-89871-661-0 (9780898716610)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Igor Griva received a B.Sc. and M.S. degree in applied mathematics in 1993 and 1994 from Moscow State University, Russia; and a Ph.D. in information technology in 2002 from George Mason University, where he is now an Assistant Professor of Computational Sciences and Mathematics in the College of Science. Prior to coming to George Mason University, he was a research associate at the Department of Financial Engineering and Operations Research in Princeton University. His research focuses on theory and methods of nonlinear optimization and their application to problems in science and engineering. Stephen Nash received a B.Sc. (Honors) degree in mathematics in 1977 from the University of Alberta, Canada; and a Ph.D. in computer science in 1982 from Stanford University. He is the Program Director for the Operations Research program at the National Science Foundation, on leave from George Mason University. Dr Nash is a Professor of Systems Engineering and Operations Research in the Volgenau School of Information Technology and Engineering. Prior to coming to George Mason University, he taught at The Johns Hopkins University. He has also had professional associations with the National Institute of Standards and Technology and the Argonne National Laboratory. His research activities are centered in scientific computing, especially nonlinear optimization, along with related interests in statistical computing and optimal control. He has been a member of the editorial boards of Computers in Science & Engineering, the SIAM Journal on Scientific Computing, Operations Research, and the Journal of the American Statistical Association. Ariela Sofer received the B.Sc. in mathematics, and the M.Sc. in operations research from the Technion in Israel. She received the D.Sc. degree in operations research from the George Washington University in 1984. She is Professor and Chair of the Systems Engineering and Operations Research Department at George Mason University. Her major areas of interest are nonlinear optimization, and optimization in biomedical applications. She has been a member of the editorial boards of the journals Operations Research and Management Science, and is coeditor on a subseries of the Annals of Operations Research on Operations Research in Medicine.
Content
Preface
Part I: Basics
Chapter 1: Optimization Models
Chapter 2: Fundamentals of Optimization
Chapter 3: Representation of Linear Constraints
Part II: Linear Programming
Chapter 4: Geometry of Linear Programming
Chapter 5: The Simplex Method
Chapter 6: Duality and Sensitivity
Chapter 7: Enhancements of the Simplex Method
Chapter 8: Network Problems
Chapter 9: Computational Complexity of Linear Programming
Chapter 10: Interior-Point Methods of Linear Programming
Part III: Unconstrained Optimization
Chapter 11: Basics of Unconstrained Optimization
Chapter 12: Methods for Unconstrained Optimization
Chapter 13: Low-Storage Methods for Unconstrained Problems
Part IV: Nonlinear Optimization
Chapter 14: Optimality Conditions for Constrained Problems
Chapter 15: Feasible-Point Methods
Chapter 16: Penalty and Barrier Methods
Part V: Appendices
Appendix A: Topics from Linear Algebra
Appendix B: Other Fundamentals
Appendix C: Software
Bibliography
Index
Part I: Basics
Chapter 1: Optimization Models
Chapter 2: Fundamentals of Optimization
Chapter 3: Representation of Linear Constraints
Part II: Linear Programming
Chapter 4: Geometry of Linear Programming
Chapter 5: The Simplex Method
Chapter 6: Duality and Sensitivity
Chapter 7: Enhancements of the Simplex Method
Chapter 8: Network Problems
Chapter 9: Computational Complexity of Linear Programming
Chapter 10: Interior-Point Methods of Linear Programming
Part III: Unconstrained Optimization
Chapter 11: Basics of Unconstrained Optimization
Chapter 12: Methods for Unconstrained Optimization
Chapter 13: Low-Storage Methods for Unconstrained Problems
Part IV: Nonlinear Optimization
Chapter 14: Optimality Conditions for Constrained Problems
Chapter 15: Feasible-Point Methods
Chapter 16: Penalty and Barrier Methods
Part V: Appendices
Appendix A: Topics from Linear Algebra
Appendix B: Other Fundamentals
Appendix C: Software
Bibliography
Index