LEADER 03669nam 22005655 450 001 9910254294603321 005 20250523221720.0 010 $a3-319-68913-4 024 7 $a10.1007/978-3-319-68913-5 035 $a(CKB)4100000001381586 035 $a(DE-He213)978-3-319-68913-5 035 $a(MiAaPQ)EBC6310666 035 $a(MiAaPQ)EBC5590869 035 $a(Au-PeEL)EBL5590869 035 $a(OCoLC)1014337194 035 $a(PPN)222228253 035 $a(EXLCZ)994100000001381586 100 $a20171202d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDerivative-Free and Blackbox Optimization /$fby Charles Audet, Warren Hare 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVIII, 302 p. 38 illus.) 225 1 $aSpringer Series in Operations Research and Financial Engineering,$x2197-1773 311 08$a3-319-68912-6 327 $aPart I: Introduction and Background Material -- Introduction: Tools and Challenges -- Mathematical Background -- The Beginnings of DFO Algorithms -- Part I: Some Remarks on DFO -- Part II: Popular Heuristic Methods -- Genetic Algorithms -- Nelder-Mead -- Part II: Further Remarks on Heuristics -- Part III: Direct Search Methods -- Positive bases and Nonsmooth Optimization -- Generalized Pattern Search -- Mesh Adaptive Direct Search -- Part III: Further Remarks on Direct Search Methods -- Part IV: Model-based Methods -- Model-based Descent -- Model-based Trust Region -- Part IV: Further Remarks on Model-based Methods -- Part V: Extensions and Refinements -- Variables and Constraints -- Optimization Using Surrogates and Models -- Biobjective Optimization -- Part V: Final Remarks on DFO/BBO -- Part VI: Appendix: Comparing Optimization Methods -- Solutions to Selected Exercises. 330 $aThis book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead). Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region). Part V discusses dealing with constraints, using surrogates, and bi-objective optimization. End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures. Benchmarking techniques are also presented in the appendix. 410 0$aSpringer Series in Operations Research and Financial Engineering,$x2197-1773 606 $aMathematical optimization 606 $aNumerical analysis 606 $aOptimization 606 $aNumerical Analysis 615 0$aMathematical optimization. 615 0$aNumerical analysis. 615 14$aOptimization. 615 24$aNumerical Analysis. 676 $a519.3 700 $aAudet$b Charles$4aut$4http://id.loc.gov/vocabulary/relators/aut$0767618 702 $aHare$b Warren$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254294603321 996 $aDerivative-Free and Blackbox Optimization$91984160 997 $aUNINA