01251nam0 22002891i 450 UON0002725320231205102044.89381-85616-39-620020107d1996 |0itac50 baengIN|||| 1||||INDIA's Cultural Relations with South-East Asiaeditors Manjushree Rao, Susmita Pande , Bhaskar Nath MisraDelhiSharada Publishing House1996 xv202 p.ill. , tavole ; 24 cm Seminario tenuto a Bhopal 27-29 marzo 1990SUDEST ASIATICOSTORIA E CULTURAINFLUSSI INDIANIUONC009311FIINNew DelhiUONL000110SEA GEN E ISUDEST ASIATICO - CONGRESSIAMISRAB. N.UONV014230PANDESusmitaUONV014268RAOManjushreeUONV014084Sharada PrakashanaUONV249409650ITSOL20240220RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00027253SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI SEA GEN E I 005 SI SA 85071 7 005 INDIA's Cultural Relations with South-East Asia1200200UNIOR03697nam 2200685z- 450 991055710780332120210501(CKB)5400000000040969(oapen)https://directory.doabooks.org/handle/20.500.12854/69429(oapen)doab69429(EXLCZ)99540000000004096920202105d2020 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierNonsmooth Optimization in Honor of the 60th Birthday of Adil M. BagirovBasel, SwitzerlandMDPI - Multidisciplinary Digital Publishing Institute20201 online resource (116 p.)3-03943-835-2 3-03943-836-0 The aim of this book was to collect the most recent methods developed for NSO and its practical applications. The book contains seven papers: The first is the foreword by the Guest Editors giving a brief review of NSO and its real-life applications and acknowledging the outstanding contributions of Professor Adil Bagirov to both the theoretical and practical aspects of NSO. The second paper introduces a new and very efficient algorithm for solving uncertain unit-commitment (UC) problems. The third paper proposes a new nonsmooth version of the generalized damped Gauss-Newton method for solving nonlinear complementarity problems. In the fourth paper, the abs-linear representation of piecewise linear functions is extended to yield simultaneously their DC decomposition as well as the pair of generalized gradients. The fifth paper presents the use of biased-randomized algorithms as an effective methodology to cope with NP-hard and nonsmooth optimization problems in many practical applications. In the sixth paper, a problem concerning the scheduling of nuclear waste disposal is modeled as a nonsmooth multiobjective mixed-integer nonlinear optimization problem, and a novel method using the two-slope parameterized achievement scalarizing functions is introduced. Finally, the last paper considers binary classification of a multiple instance learning problem and formulates the learning problem as a nonconvex nonsmooth unconstrained optimization problem with a DC objective function.Information technology industriesbicsscabs-linearizationachievement scalarizing functionsasynchronous computingB-differentialbiased-randomized algorithmsDC functionDC optimizationDCAGauss-Newton methodglobal convergenceheuristicsinteractive methodlevel decompositionmultiobjective optimizationmultiple instance learningn/anon-smooth optimizationnonlinear complementarity problemnonsmooth equationsnonsmooth optimizationparallel computingsoft constraintsspent nuclear fuel disposalstochastic hydrothermal UC problemstochastic programmingsuperlinear convergencesupport vector machineInformation technology industriesKarmitsa Napsuedt1296135Taheri SonaedtKarmitsa NapsuothTaheri SonaothBOOK9910557107803321Nonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov3023795UNINA