| 期刊全稱(chēng) | BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems | | 影響因子2023 | Urmila Diwekar,Amy David | | 視頻video | http://file.papertrans.cn/181/180135/180135.mp4 | | 發(fā)行地址 | Incorporates the BONUS algorithm into real world applications.Characterizes a fast algorithm for large scale stochastic nonlinear programming problems.Describes a new technique that can be used in are | | 學(xué)科分類(lèi) | SpringerBriefs in Optimization | | 圖書(shū)封面 |  | | 影響因子 | 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 th | | Pindex | Book 2015 |
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