LEADER 01219nam0 22002653i 450 001 VAN0098555 005 20140709024557.454 010 $a88-14-11409-9 100 $a20140708d2004 |0itac50 ba 101 $aita 102 $aIT 105 $a|||| ||||| 200 1 $aAlcune figure di comportamento omissivo della pubblica amministrazione$espunti ricostruttivi$fGiuseppe Falzea 210 $aMilano$cGiuffrè$d2004 215 $a155 p.$d24 cm. 410 1$1001VAN0098556$12001 $aPubblicazioni del Dipartimento di diritto interno e comunitario delle amministrazioni pubbliche e del territorio$fUniversità degli studi di Messina, Facoltà di economia$1210 $aMilano$cGiuffrè. 620 $dMilano$3VANL000284 700 1$aFalzea$bGiuseppe$3VANV007311$0255348 712 $aGiuffrè $3VANV109181$4650 801 $aIT$bSOL$c20230616$gRICA 899 $aBIBLIOTECA DEL DIPARTIMENTO DI ECONOMIA$1IT-CE0106$2VAN03 912 $aVAN0098555 950 $aBIBLIOTECA DEL DIPARTIMENTO DI ECONOMIA$d03PREST IVFa50 $e03 31825 20140708 996 $aAlcune figure di comportamento omissivo della pubblica amministrazione$91411839 997 $aUNICAMPANIA LEADER 01890nam0 22004333i 450 001 VAN00245943 005 20260616041303.600 017 70$2N$a9783030857769 035 40$a1591433122 100 $a20220513d2021 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 181 $ai$b e 182 $ab 183 $acr 200 1 $aCeramics, Glass and Glass-Ceramics$eFrom Early Manufacturing Steps Towards Modern Frontiers$feditors Francesco Baino, Massimo Tomalino, Dilshat Tulyaganov 210 $aCham$cSpringer$d2021 215 $aVIII, 343 p.$cill.$d24 cm 410 1$1001VAN00114405$12001 $aPoliTO Springer Series$fPolitecnico di Torino$1210 $aBerlin$cSpringer$d2016- 620 $aCH$dCham$3VANL001889 676 $a621.366$cFisica applicata. Laser spettroscopia$v22 676 $a668.4192$cTecnologia dei rifiuti$v22 676 $a535.2$cOttica fisica$v22 676 $a620.1$cScienze dei materiali$v22 676 $a620.14$cCeramica e materiali affini$v22 676 $a621.365$cFotonica$v22 676 $a621.48332$cMateriali strutturali$v22 702 1$aBaino$bFrancesco$3VANV200831 702 1$aTomalino$bMassimo$3VANV200833 702 1$aTulyaganov$bDilshat$3VANV200836 801 $aIT$bSOL$c20260619$gRICA 856 4 $uhttps://link.springer.com/book/10.1007/978-3-030-85776-9$zE-book - Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE$1IT-CE0101$2VAN17 912 $fN 912 $aVAN00245943 950 $aBIBLIOTECA DEL DIPARTIMENTO DI SCIENZE E TECNOLOGIE AMBIENTALI BIOLOGICHE E FARMACEUTICHE$d17CONS e-book 2222 $e17BIB2222/291 291 20220513 996 $aCeramics, Glass and Glass-Ceramics$92837898 997 $aUNICAMPANIA LEADER 03378nam0 2200457 i 450 001 VAN00113101 005 20260625075924.92 017 70$2N$a9781493922826 035 40$a1591483598 100 $a20171227d2015 |0itac50 ba 101 $aeng 102 $aUS 105 $a|||| ||||| 181 $ai$b e 182 $ab 183 $acr 200 1 $aBONUS algorithm for large scale stochastic nonlinear programming problems$fUrmila Diwekar, Amy David 210 $aNew York$cSpringer$d2015 215 $aXVIII, 146 p.$cill.$d24 cm 327 $aThis book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world. 410 1$1001VAN00102834$12001 $aSpringerBriefs in optimization$1210 $aBerlin [etc.]$cSpringer$d2012- 606 $a90C06$xLarge-scale problems in mathematical programming [MSC 2020]$3VANC028934$2MF 606 $a90C15$xStochastic programming [MSC 2020]$3VANC021355$2MF 610 $aBonus Algorithm$9KW:K 610 $aPower Systems$9KW:K 610 $aSNLP$9KW:K 610 $aSensor placement$9KW:K 610 $aStochastic Programming$9KW:K 610 $aWater Management$9KW:K 620 $aUS$dNew York$3VANL000011 700 1$aDiwekar$bUrmila M.$3VANV087258$0845355 701 1$aDavid$bAmy$3VANV087259$0755490 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20260911$gRICA 856 4 $uhttp://dx.doi.org/10.1007/978-1-4939-2282-6$zE-book ? Accesso al full-text attraverso riconoscimento IP di Ateneo, proxy e/o Shibboleth 899 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$1IT-CE0120$2VAN08 912 $fN 912 $aVAN00113101 950 $aBIBLIOTECA DEL DIPARTIMENTO DI MATEMATICA E FISICA$d08DLOAD e-book 0124 $e08eMF124 20171227 996 $aBONUS algorithm for large scale stochastic nonlinear programming problems$92905427 997 $aUNICAMPANIA LEADER 02395nam0 22005773i 450 001 VAN00274535 005 20260702104139.355 017 70$2N$a9783030685140 035 40$a1591433010 100 $a20240408d2021 |0itac50 ba 101 $aeng 102 $aCH 105 $a|||| ||||| 181 $ai$b e 182 $ab 183 $acr 200 1 $aAlgorithm Portfolios$eAdvances, Applications, and Challenges$fDimitris Souravlias ... [et al.] 210 $aCham$cSpringer$d2021 215 $axiv, 92 p.$cill.$d24 cm 410 1$1001VAN00102834$12001 $aSpringerBriefs in optimization$1210 $aBerlin [etc.]$cSpringer$d2012- 606 $a65-XX$xNumerical analysis [MSC 2020]$3VANC019772$2MF 606 $a65Kxx$xNumerical methods for mathematical programming, optimization and variational techniques [MSC 2020]$3VANC022719$2MF 606 $a90-XX$xOperations research, mathematical programming [MSC 2020]$3VANC025650$2MF 606 $a90Cxx$xMathematical programming [MSC 2020]$3VANC020086$2MF 610 $aAlgorithm Portfolios$9KW:K 610 $aCirculant Weighing Matrices$9KW:K 610 $aCombinatorics$9KW:K 610 $aConstituent algorithms$9KW:K 610 $aEfficient algorithm portfolios$9KW:K 610 $aFacility location$9KW:K 610 $aInventory routing$9KW:K 610 $aLot sizing$9KW:K 610 $aMarket-based algorithms$9KW:K 610 $aMetaheuristic optimization algorithms$9KW:K 610 $aMetaheuristics$9KW:K 610 $aOpen Problems$9KW:K 610 $aOptimization algorithms$9KW:K 610 $aParallel models$9KW:K 610 $aSequential models$9KW:K 610 $aTrading-based budget allocation$9KW:K 620 $aCH$dCham$3VANL001889 702 1$aSouravlias$bDimitris$3VANV226974 712 $aSpringer $3VANV108073$4650 801 $aIT$bSOL$c20260911$gRICA 856 4 $uhttps://doi.org/10.1007/978-3-030-68514-0$zE-book ? 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