MARC Record from marc_columbia

LEADER: 03467cam a2200661 i 4500
001 16155730
005 20220618233633.0
006 m o d
007 cr cnu|||unuuu
008 220425s2022 xx o 000 0 eng d
035 $a(OCoLC)on1312327233
035 $a(NNC)16155730
040 $aTYFRS$beng$erda$epn$cTYFRS$dOCLCQ$dTYFRS$dOCLCO$dOCLCF
020 $a9780203738269$q(electronic bk.)
020 $a0203738268$q(electronic bk.)
020 $a9781351413695$q(electronic bk. ;$qPDF)
020 $a1351413694$q(electronic bk. ;$qPDF)
020 $a9781351413671$q(electronic bk. ;$qMobipocket)
020 $a1351413678$q(electronic bk. ;$qMobipocket)
020 $a9781351413688$q(electronic bk. ;$qEPUB)
020 $a1351413686$q(electronic bk. ;$qEPUB)
020 $z9789056991449
024 7 $a10.1201/9780203738269$2doi
035 $a(OCoLC)1312327233
037 $a9780203738269$bTaylor & Francis
050 4 $aT57.79
072 7 $aCOM$x012040$2bisacsh
072 7 $aCOM$x051300$2bisacsh
072 7 $aMAT$x004000$2bisacsh
072 7 $aUMB$2bicssc
082 04 $a519.72$223
049 $aZCUA
100 1 $aMayer, János.
245 10 $aStochastic linear programming algorithms :$ba comparison based on a model management /$cJános Mayer.
250 $aFirst edition.
264 1 $a[Place of publication not identified] :$bRoutledge,$c2022.
300 $a1 online resource (164 pages)
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
505 0 $a1. Stochastic Linear Programming Models 2. Stochastic Linear Programming Algorithms 3. Implementation. The Testing Environment 4. Computational Results 5. Algorithmic Concepts in Convex Programming
520 $aA computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this is the first book to present comparative computational results with several major stochastic programming solution approaches. The following methods are considered: regularized decomposition, stochastic decomposition and successive discrete approximation methods for two stage problems; cutting plane methods, and a reduced gradient method for jointly chance constrained problems. The first part of the book introduces the algorithms, including a unified approach to decomposition methods and their regularized counterparts. The second part addresses computer implementation of the methods, describes a testing environment based on a model management system, and presents comparative computational results with the various algorithms. Emphasis is on the computational behavior of the algorithms.
545 0 $aJanos Mayer
588 0 $aVendor-supplied metadata.
650 0 $aStochastic programming.
650 0 $aAlgorithms.
650 2 $aAlgorithms
650 6 $aProgrammation stochastique.
650 6 $aAlgorithmes.
650 7 $aalgorithms.$2aat
650 7 $aCOMPUTERS$xComputer Graphics$xGame Programming & Design.$2bisacsh
650 7 $aCOMPUTERS$xProgramming$xAlgorithms.$2bisacsh
650 7 $aMATHEMATICS$xArithmetic.$2bisacsh
650 7 $aAlgorithms.$2fast$0(OCoLC)fst00805020
650 7 $aStochastic programming.$2fast$0(OCoLC)fst01133530
655 4 $aElectronic books.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio16155730$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS