TY - JOUR

T1 - Exponential lower bounds for the running time of DPLL algorithms on satisfiable formulas

AU - Alekhnovich, Michael

AU - Hirsch, Edward A.

AU - Itsykson, Dmitry

PY - 2004

Y1 - 2004

N2 - DPLL algorithms form the largest family of contemporary algorithms for SAT (the prepositional satisfiability problem) and are widely used in applications. The recursion trees of DPLL algorithm executions on unsatisfiable formulas are equivalent to tree-like resolution proofs. Therefore, lower bounds for tree-like resolution (known since 1960s) apply to them. However, these lower bounds say nothing about their behavior on satisfiable formulas. Proving exponential lower bounds for them in the most general setting is impossible without proving P ≠ NP; thus, in order to prove lower bounds one has to restrict the power of branching heuristics. In this paper, we give exponential lower bounds for two families of DPLL algorithms: generalized myopic algorithms (that read up to n1-ε of clauses at each step and see the remaining part of the formula without negations) and drunk algorithms (that choose a variable using any complicated rule and then pick its value at random).

AB - DPLL algorithms form the largest family of contemporary algorithms for SAT (the prepositional satisfiability problem) and are widely used in applications. The recursion trees of DPLL algorithm executions on unsatisfiable formulas are equivalent to tree-like resolution proofs. Therefore, lower bounds for tree-like resolution (known since 1960s) apply to them. However, these lower bounds say nothing about their behavior on satisfiable formulas. Proving exponential lower bounds for them in the most general setting is impossible without proving P ≠ NP; thus, in order to prove lower bounds one has to restrict the power of branching heuristics. In this paper, we give exponential lower bounds for two families of DPLL algorithms: generalized myopic algorithms (that read up to n1-ε of clauses at each step and see the remaining part of the formula without negations) and drunk algorithms (that choose a variable using any complicated rule and then pick its value at random).

UR - http://www.scopus.com/inward/record.url?scp=35048869086&partnerID=8YFLogxK

U2 - 10.1007/978-3-540-27836-8_10

DO - 10.1007/978-3-540-27836-8_10

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AN - SCOPUS:35048869086

SN - 0302-9743

SP - 84

EP - 96

JO - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

JF - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

ER -