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Books > Science & Mathematics > Mathematics > Combinatorics & graph theory
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Bayesian Heuristic Approach to Discrete and Global Optimization - Algorithms, Visualization, Software, and Applications (Hardcover, 1997 ed.)
Loot Price: R6,726
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Bayesian Heuristic Approach to Discrete and Global Optimization - Algorithms, Visualization, Software, and Applications (Hardcover, 1997 ed.)
Series: Nonconvex Optimization and Its Applications, 17
Expected to ship within 18 - 22 working days
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Bayesian decision theory is known to provide an effective framework
for the practical solution of discrete and nonconvex optimization
problems. This book is the first to demonstrate that this framework
is also well suited for the exploitation of heuristic methods in
the solution of such problems, especially those of large scale for
which exact optimization approaches can be prohibitively costly.
The book covers all aspects ranging from the formal presentation of
the Bayesian Approach, to its extension to the Bayesian Heuristic
Strategy, and its utilization within the informal, interactive
Dynamic Visualization strategy. The developed framework is applied
in forecasting, in neural network optimization, and in a large
number of discrete and continuous optimization problems. Specific
application areas which are discussed include scheduling and
visualization problems in chemical engineering, manufacturing
process control, and epidemiology. Computational results and
comparisons with a broad range of test examples are presented. The
software required for implementation of the Bayesian Heuristic
Approach is included. Although some knowledge of mathematical
statistics is necessary in order to fathom the theoretical aspects
of the development, no specialized mathematical knowledge is
required to understand the application of the approach or to
utilize the software which is provided. Audience: The book is of
interest to both researchers in operations research, systems
engineering, and optimization methods, as well as applications
specialists concerned with the solution of large scale discrete
and/or nonconvex optimization problems in a broad range of
engineering and technological fields. It may be used as
supplementary material for graduate level courses.
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