LEADER 01497nas 2200445 a 450 001 9910400559903321 005 20240413012243.0 035 $a(CKB)954925402592 035 $a(CONSER)it 01000053 035 $a(DE-599)ZDB2228386-9 035 $a(EXLCZ)99954925402592 100 $a20750305a18839999 uy b 101 0 $aita 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aGiornale storico della letteratura italiana 210 $aTorino $cLoescher$d1883- 215 $a1 online resource 300 $aImprint varies. 311 08$aPrint version: Giornale storico della letteratura italiana. 0017-0496 (DLC)it 01000053 (OCoLC)566133549 531 $aGIORN STORICO LETT 531 0 $aG. stor. lett. ital. 606 $aItalian literature$xHistory and criticism$vPeriodicals 606 $aLitte?rature italienne$xHistoire et critique$vPe?riodiques 606 $aLiterature$2gtt$3(NL-LeOCL)078573246 606 $aItalian$3(NL-LeOCL)078550521$2gtt 606 $aRomanistik$2swd 606 $aLiteraturwissenschaft$2swd 615 0$aItalian literature$xHistory and criticism 615 6$aLitte?rature italienne$xHistoire et critique 615 17$aLiterature. 615 17$aItalian. 615 07$aRomanistik. 615 07$aLiteraturwissenschaft. 906 $aJOURNAL 912 $a9910400559903321 920 $aexl_impl conversion 996 $aGiornale storico della letteratura italiana$9796056 997 $aUNINA LEADER 05347nam 22007335 450 001 9911001787603321 005 20250506125928.0 010 $a3-031-84837-3 024 7 $a10.1007/978-3-031-84837-7 035 $a(CKB)38753876500041 035 $a(DE-He213)978-3-031-84837-7 035 $a(MiAaPQ)EBC32256093 035 $a(Au-PeEL)EBL32256093 035 $a(OCoLC)1534199136 035 $a(EXLCZ)9938753876500041 100 $a20250506d2025 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMethods of Nonsmooth Optimization in Stochastic Programming $eFrom Conceptual Algorithms to Real-World Applications /$fby Wim Stefanus van Ackooij, Welington Luis de Oliveira 205 $a1st ed. 2025. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025. 215 $a1 online resource (XVI, 570 p. 39 illus., 30 illus. in color.) 225 1 $aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v363 311 08$a3-031-84836-5 327 $aIntroduction -- Primer of convex analysis -- Variational analysis -- Linear and nonlinear optimization problems -- Probability and Statistics -- Fundamental modeling questions in stochastic programming -- Adjusting to uncertainty: modeling recourse -- Probability constraints -- Proximal point algorithms for problems with structure -- Cutting-plane algorithms for nonsmooth convex optimization over simple domains -- Bundle methods for nonsmooth convex optimization over simple domains -- Methods for nonlinearly constrained nonsmooth optimization problems -- Methods for nonsmooth optimization with mixed-integer variables -- Methods for nonsmooth nonconvex optimization -- Two-stage stochastic programs -- Progressive decoupling in multistage stochastic programming -- Scenario decomposition with alternating projections -- Methods for multistage stochastic linear programs -- Methods for handling probability. 330 $aThis book presents a comprehensive series of methods in nonsmooth optimization, with a particular focus on their application in stochastic programming and dedicated algorithms for decision-making under uncertainty. Each method is accompanied by rigorous mathematical analysis, ensuring a deep understanding of the underlying principles. The theoretical discussions included are essential for comprehending the mechanics of various algorithms and the nature of the solutions they provide?whether they are global, local, stationary, or critical. The book begins by introducing fundamental tools from set-valued analysis, optimization, and probability theory. It then transitions from deterministic to stochastic optimization, starting with a thorough discussion of modeling, understanding uncertainty, and incorporating it into optimization problems. Following this foundation, the book explores numerical algorithms for nonsmooth optimization, covering well-known decomposition techniques and algorithms for convex optimization, mixed-integer convex programming, and nonconvex optimization. Additionally, it introduces numerical algorithms specifically for stochastic programming, focusing on stochastic programming with recourse, chance-constrained optimization, and detailed algorithms for both risk-neutral and risk-averse multistage stochastic programs. The book guides readers through the entire process, from defining optimization models for practical problems to presenting implementable algorithms that can be applied in practice. It is intended for students, practitioners, and scholars who may be unfamiliar with stochastic programming and nonsmooth optimization. The analyses provided are also valuable for practitioners who may not be interested in convergence proofs but wish to understand the nature of the solutions obtained. 410 0$aInternational Series in Operations Research & Management Science,$x2214-7934 ;$v363 606 $aOperations research 606 $aMathematical optimization 606 $aManagement science 606 $aNumerical analysis 606 $aStochastic processes 606 $aCalculus 606 $aOperations Research and Decision Theory 606 $aOptimization 606 $aOperations Research, Management Science 606 $aNumerical Analysis 606 $aContinuous Optimization 606 $aStochastic Calculus 615 0$aOperations research. 615 0$aMathematical optimization. 615 0$aManagement science. 615 0$aNumerical analysis. 615 0$aStochastic processes. 615 0$aCalculus. 615 14$aOperations Research and Decision Theory. 615 24$aOptimization. 615 24$aOperations Research, Management Science. 615 24$aNumerical Analysis. 615 24$aContinuous Optimization. 615 24$aStochastic Calculus. 676 $a658.403 700 $aAckooij$b Wim van$4aut$4http://id.loc.gov/vocabulary/relators/aut$01888259 702 $ade Oliveira$b Welington Luis$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911001787603321 996 $aMethods of Nonsmooth Optimization in Stochastic Programming$94526781 997 $aUNINA