| Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|---|
| E-Books | Biblioteca da FCTUNL Online | Não Ficção | T57.79.SPR FCT 96331 (Browse shelf) | 1 | Available |
Browsing Biblioteca da FCTUNL Shelves , Shelving location: Online , Collection code: Não Ficção Close shelf browser
| T57.79.SPR FCT 81311 Introduction to stochastic programming | T57.79.SPR FCT 82803 Stability, approximation, and decomposition in two- and multistage stochastic programming | T57.79.SPR FCT 82807 A scenario tree-based decomposition for solving multistage stochastic programs | T57.79.SPR FCT 96331 BONUS algorithm for large scale stochastic nonlinear programming problems | T57.8. SPR FCT 94185 Nonlinear optimization with financial applications | T57.8.SPR FCT 94570 Optimization with multivalued mappings | T57.8.SPR FCT 94840 Nonlinear integer programming |
Colocação: Online
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.
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