A Set Of Examples Of Global And Discrete Optimization: Applications Of Bayesian Heuristic Approach (applied Optimization)
by Jonas Mockus /
2013 / English / PDF
9.6 MB Download
This book shows how the Bayesian Approach (BA) improves well known
heuristics by randomizing and optimizing their parameters. That is
the Bayesian Heuristic Approach (BHA). The ten in-depth examples
are designed to teach Operations Research using Internet. Each
example is a simple representation of some impor tant family of
real-life problems. The accompanying software can be run by remote
Internet users. The supporting web-sites include software for Java,
C++, and other lan guages. A theoretical setting is described in
which one can discuss a Bayesian adaptive choice of heuristics for
discrete and global optimization prob lems. The techniques are
evaluated in the spirit of the average rather than the worst case
analysis. In this context, "heuristics" are understood to be an
expert opinion defining how to solve a family of problems of dis
crete or global optimization. The term "Bayesian Heuristic
Approach" means that one defines a set of heuristics and fixes some
prior distribu tion on the results obtained. By applying BHA one
is looking for the heuristic that reduces the average deviation
from the global optimum. The theoretical discussions serve as an
introduction to examples that are the main part of the book. All
the examples are interconnected. Dif ferent examples illustrate
different points of the general subject. How ever, one can
consider each example separately, too.
This book shows how the Bayesian Approach (BA) improves well known
heuristics by randomizing and optimizing their parameters. That is
the Bayesian Heuristic Approach (BHA). The ten in-depth examples
are designed to teach Operations Research using Internet. Each
example is a simple representation of some impor tant family of
real-life problems. The accompanying software can be run by remote
Internet users. The supporting web-sites include software for Java,
C++, and other lan guages. A theoretical setting is described in
which one can discuss a Bayesian adaptive choice of heuristics for
discrete and global optimization prob lems. The techniques are
evaluated in the spirit of the average rather than the worst case
analysis. In this context, "heuristics" are understood to be an
expert opinion defining how to solve a family of problems of dis
crete or global optimization. The term "Bayesian Heuristic
Approach" means that one defines a set of heuristics and fixes some
prior distribu tion on the results obtained. By applying BHA one
is looking for the heuristic that reduces the average deviation
from the global optimum. The theoretical discussions serve as an
introduction to examples that are the main part of the book. All
the examples are interconnected. Dif ferent examples illustrate
different points of the general subject. How ever, one can
consider each example separately, too.