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Books > Business & Economics > Business & management > Management & management techniques > Operational research
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BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems (Paperback, 2015 ed.)
Loot Price: R1,793
Discovery Miles 17 930
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BONUS Algorithm for Large Scale Stochastic Nonlinear Programming Problems (Paperback, 2015 ed.)
Series: SpringerBriefs in Optimization
Expected to ship within 18 - 22 working days
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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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