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BONUS algorithm for large scale stochastic nonlinear programming problems / Urmila Diwekar, Amy David
| BONUS algorithm for large scale stochastic nonlinear programming problems / Urmila Diwekar, Amy David |
| Autore | Diwekar, Urmila M. |
| Pubbl/distr/stampa | New York, : Springer, 2015 |
| Descrizione fisica | XVIII, 146 p. : ill. ; 24 cm |
| Altri autori (Persone) | David, Amy |
| Soggetto topico |
90C15 - Stochastic programming [MSC 2020]
90C06 - Large-scale problems in mathematical programming [MSC 2020] |
| Soggetto non controllato |
BONUS algorithm
Power systems SNLP Sensor placement Stochastic Programming Water management |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Titolo uniforme | |
| Record Nr. | UNICAMPANIA-VAN0113101 |
Diwekar, Urmila M.
|
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| New York, : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||
BONUS algorithm for large scale stochastic nonlinear programming problems / Urmila Diwekar, Amy David
| BONUS algorithm for large scale stochastic nonlinear programming problems / Urmila Diwekar, Amy David |
| Autore | Diwekar, Urmila M. |
| Pubbl/distr/stampa | New York, : Springer, 2015 |
| Descrizione fisica | XVIII, 146 p. : ill. ; 24 cm |
| Altri autori (Persone) | David, Amy |
| Soggetto topico |
90C06 - Large-scale problems in mathematical programming [MSC 2020]
90C15 - Stochastic programming [MSC 2020] |
| Soggetto non controllato |
BONUS algorithm
Power Systems SNLP Sensor placement Stochastic Programming Water Management |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | This book presents the details of the BONUS algorithm and its real world applications in areas like sensor placement in large scale drinking water networks, sensor placement in advanced power systems, water management in power systems, and capacity expansion of energy systems. A generalized method for stochastic nonlinear programming based on a sampling based approach for uncertainty analysis and statistical reweighting to obtain probability information is demonstrated in this book. Stochastic optimization problems are difficult to solve since they involve dealing with optimization and uncertainty loops. There are two fundamental approaches used to solve such problems. The first being the decomposition techniques and the second method identifies problem specific structures and transforms the problem into a deterministic nonlinear programming problem. These techniques have significant limitations on either the objective function type or the underlying distributions for the uncertain variables. Moreover, these methods assume that there are a small number of scenarios to be evaluated for calculation of the probabilistic objective function and constraints. This book begins to tackle these issues by describing a generalized method for stochastic nonlinear programming problems. This title is best suited for practitioners, researchers and students in engineering, operations research, and management science who desire a complete understanding of the BONUS algorithm and its applications to the real world. |
| Record Nr. | UNICAMPANIA-VAN00113101 |
|
Diwekar, Urmila M.
|
||
| New York, : Springer, 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Vanvitelli | ||
| ||