LEADER 05872nam 22006615 450 001 9910254898803321 005 20200702173842.0 010 $a1-4939-7055-0 024 7 $a10.1007/978-1-4939-7055-1 035 $a(CKB)4340000000061752 035 $a(DE-He213)978-1-4939-7055-1 035 $a(MiAaPQ)EBC5592635 035 $a(PPN)222233303 035 $a(EXLCZ)994340000000061752 100 $a20170611d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLinear and Nonlinear Optimization /$fby Richard W. Cottle, Mukund N. Thapa 205 $a1st ed. 2017. 210 1$aNew York, NY :$cSpringer New York :$cImprint: Springer,$d2017. 215 $a1 online resource (XXXI, 614 p. 58 illus.) 225 1 $aInternational Series in Operations Research & Management Science,$x0884-8289 ;$v253 311 08$a1-4939-7053-4 320 $aIncludes bibliographical references (pages 587-602) and index. 327 $aChapter 1. LP Models and Applications -- Chapter 2. Linear Equations and Inequalities -- Chapter 3. The Simplex Algorithm -- Chapter 4. The Simplex Algorithm Continued -- Chapter 5. Duality and the Dual Simplex Algorithm -- Chapter 6. Postoptimality Analysis -- Chapter 7. Some Computational Considerations -- Chapter 8. NLP Models and Applications -- Chapter 9. Unconstrained Optimization -- Chapter 10. Descent Methods -- Chapter 11. Optimality Conditions -- Chapter 12. Problems with Linear Constraints -- Chapter 13. Problems with Nonlinear Constraints -- Chapter 14. Interior-Point Methods. 330 $aThis 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. 410 0$aInternational Series in Operations Research & Management Science,$x0884-8289 ;$v253 606 $aOperations research 606 $aDecision making 606 $aManagement science 606 $aMathematical optimization 606 $aMathematical models 606 $aOperations Research/Decision Theory$3https://scigraph.springernature.com/ontologies/product-market-codes/521000 606 $aOperations Research, Management Science$3https://scigraph.springernature.com/ontologies/product-market-codes/M26024 606 $aOptimization$3https://scigraph.springernature.com/ontologies/product-market-codes/M26008 606 $aMathematical Modeling and Industrial Mathematics$3https://scigraph.springernature.com/ontologies/product-market-codes/M14068 615 0$aOperations research. 615 0$aDecision making. 615 0$aManagement science. 615 0$aMathematical optimization. 615 0$aMathematical models. 615 14$aOperations Research/Decision Theory. 615 24$aOperations Research, Management Science. 615 24$aOptimization. 615 24$aMathematical Modeling and Industrial Mathematics. 676 $a658.403 700 $aCottle$b Richard W$4aut$4http://id.loc.gov/vocabulary/relators/aut$0104963 702 $aThapa$b Mukund N$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254898803321 996 $aLinear and Nonlinear Optimization$92252194 997 $aUNINA