“Probabilistic Greedy Algorithms for Satisfiability Problems”
Coauthor(s): Ramesh Krishnamurti.
Editors: Teofilo F. Gonzalez
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We examine probabilistic greedy heuristics for maximization and
minimization versions of the satisfiability problem. Like deterministic
greedy algorithms, these heuristics construct a truth assignment one
variable at a time. Unlike them, they set a variable true or false using a probabilistic mechanism, the probabilities of a true assignment
depending on the incremental number of clauses satisfied if a variable
is set true. We discuss alternative probabilistic functions, and characterize the expected performance of the simplest of these rules relative
to optimal solutions. We discuss the advantages of probabilistic algorithms in general, and the probabilistic algorithms we analyze in
Source: Handbook of Approximation Algorithms and Metaheuristics
Kohli, Rajeev, and Ramesh Krishnamurti. "Probabilistic Greedy Algorithms for Satisfiability Problems." In Handbook of Approximation Algorithms and Metaheuristics. Ed. Teofilo F. Gonzalez. New York: Chapman & Hall/CRC, May 2007.
Place: New York