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Combinatorial Search: From Algorithms to Systems (Paperback, Softcover reprint of the original 1st ed. 2013)
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Combinatorial Search: From Algorithms to Systems (Paperback, Softcover reprint of the original 1st ed. 2013)
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Although they are believed to be unsolvable in general,
tractability results suggest that some practical NP-hard problems
can be efficiently solved. Combinatorial search algorithms are
designed to efficiently explore the usually large solution space of
these instances by reducing the search space to feasible regions
and using heuristics to efficiently explore these regions. Various
mathematical formalisms may be used to express and tackle
combinatorial problems, among them the constraint satisfaction
problem (CSP) and the propositional satisfiability problem (SAT).
These algorithms, or constraint solvers, apply search space
reduction through inference techniques, use activity-based
heuristics to guide exploration, diversify the searches through
frequent restarts, and often learn from their mistakes. In this
book the author focuses on knowledge sharing in combinatorial
search, the capacity to generate and exploit meaningful
information, such as redundant constraints, heuristic hints, and
performance measures, during search, which can dramatically improve
the performance of a constraint solver. Information can be shared
between multiple constraint solvers simultaneously working on the
same instance, or information can help achieve good performance
while solving a large set of related instances. In the first case,
information sharing has to be performed at the expense of the
underlying search effort, since a solver has to stop its main
effort to prepare and commu nicate the information to other
solvers; on the other hand, not sharing information can incur a
cost for the whole system, with solvers potentially exploring
unfeasible spaces discovered by other solvers. In the second case,
sharing performance measures can be done with little overhead, and
the goal is to be able to tune a constraint solver in relation to
the characteristics of a new instance - this corresponds to the
selection of the most suitable algorithm for solving a given
instance. The book is suitable for researchers, practitioners, and
graduate students working in the areas of optimization, search,
constraints, and computational complexity.
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