stochastic linear programming algorithms de mayer janos (25 résultats)

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Original-Pappband. Etat : Sehr gut. 4 Original-Pappband en Mathematik, Naturwissenschaften (Optimization Theory and Applications. Vol. 1); IX pp., 153 pp.

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Hardcover. Etat : new. Hardcover. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Etat : New. pp. 163 1st Edition.

Etat : New. 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. . . . .

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Etat : New. pp. 163.

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Hardback. Etat : New. 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.

Hardback. Etat : New. 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.

Etat : New. 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. . . . . Books ship from the US and Ireland.

Etat : New. Janos MayerA 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 stochasti.

Buch. Etat : Neu. Neuware - 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.

Hardback. Etat : New. 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.

Hardcover. Etat : new. Hardcover. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardback. Etat : New. 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.

Etat : New. pp. 163 This item is printed on demand.