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A computationally oriented comparison of solution algorithms for two stage and jointly chance constrained stochastic linear programming problems, this resource presents comparative computational results with several major stochastic programming solution approaches. Num Pages: 163 pages, Illustrations. BIC Classification: PBWL; UM; UYA. Category: (P) Professional & Vocational. Dimension: 254 x 190 x 16. Weight in Grams: 522. . 1998. 1st Edition. Hardcover. . . . . Codice articolo V9789056991449
A 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.
Informazioni sull?autore: Janos Mayer
Titolo: Stochastic Linear Programming Algorithms
Casa editrice: Taylor & Francis Ltd
Data di pubblicazione: 1998
Legatura: Rilegato
Condizione: New
Edizione: prima edizione