LEADER 03662nam 2200661z- 450 001 9910557107803321 005 20231214132948.0 035 $a(CKB)5400000000040969 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/69429 035 $a(EXLCZ)995400000000040969 100 $a20202105d2020 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aNonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2020 215 $a1 electronic resource (116 p.) 311 $a3-03943-835-2 311 $a3-03943-836-0 330 $aThe 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. 606 $aInformation technology industries$2bicssc 610 $amultiple instance learning 610 $asupport vector machine 610 $aDC optimization 610 $anonsmooth optimization 610 $aachievement scalarizing functions 610 $ainteractive method 610 $amultiobjective optimization 610 $aspent nuclear fuel disposal 610 $anon-smooth optimization 610 $abiased-randomized algorithms 610 $aheuristics 610 $asoft constraints 610 $aDC function 610 $aabs-linearization 610 $aDCA 610 $aGauss-Newton method 610 $anonsmooth equations 610 $anonlinear complementarity problem 610 $aB-differential 610 $asuperlinear convergence 610 $aglobal convergence 610 $astochastic programming 610 $astochastic hydrothermal UC problem 610 $aparallel computing 610 $aasynchronous computing 610 $alevel decomposition 615 7$aInformation technology industries 700 $aKarmitsa$b Napsu$4edt$01296135 702 $aTaheri$b Sona$4edt 702 $aKarmitsa$b Napsu$4oth 702 $aTaheri$b Sona$4oth 906 $aBOOK 912 $a9910557107803321 996 $aNonsmooth Optimization in Honor of the 60th Birthday of Adil M. Bagirov$93023795 997 $aUNINA