Journal of the ACM (JACM)
Bounded arithmetic, propositional logic, and complexity theory
Bounded arithmetic, propositional logic, and complexity theory
Lower bounds for the polynomial calculus
Computational Complexity
A lower bound for DLL algorithms for k-SAT (preliminary version)
SODA '00 Proceedings of the eleventh annual ACM-SIAM symposium on Discrete algorithms
Short proofs are narrow—resolution made simple
Journal of the ACM (JACM)
Regular resolution lower bounds for the weak pigeonhole principle
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Resolution lower bounds for the weak pigeonhole principle
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Resolution Proofs of Matching Principles
Annals of Mathematics and Artificial Intelligence
Lower Bounds for Propositional Proofs and Independence Results in Bounded Arithmetic
ICALP '96 Proceedings of the 23rd International Colloquium on Automata, Languages and Programming
Resolution and the Weak Pigeonhole Principle
CSL '97 Selected Papers from the11th International Workshop on Computer Science Logic
Pseudorandom generators in propositional proof complexity
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Resolution lower bounds for the weak functional pigeonhole principle
Theoretical Computer Science - Logic and complexity in computer science
A combinatorial characterization of resolution width
Journal of Computer and System Sciences
Width versus size in resolution proofs
TAMC'06 Proceedings of the Third international conference on Theory and Applications of Models of Computation
Parameterized Bounded-Depth Frege Is not Optimal
ACM Transactions on Computation Theory (TOCT)
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For an arbitrary hypergraph H, let PM(H) be the propositional formula asserting that H contains a perfect matching. We show that every resolution refutation of PM(H) must have size exp(Ω(δ(H)/λ(H)r(H)(log n(H))(r(H) + log n(H)))), where n(H) is the number of vertices, δ(H) is the minimal degree of a vertex, r(H) is the maximal size of an edge, and λ(H) is the maximal number of edges incident to two different vertices.For ordinary graphs G our general bound considerably simplifies to exp(Ω(δ(G)/(log n(G))2)) (implying an exp(Ω(δ(G)1/3)) lower bound that depends on the minimal degree only). As a direct corollary, every resolution proof of the functional onto version of the pigeonhole principle onto - FPHPmn must have size exp(Ω(n/(log m)2)) (which becomes exp(ω(n1/3)) when the number of pigeons m is unbounded). This in turn immediately implies an exp(Ω(t/n3)) lower bound on the size of resolution proofs of the principle asserting that the circuit size of the Boolean function fn in n variables is greater than t. In particular, Resolution does not possess efficient proofs of NP ⊈ P/poly.These results relativize, in a natural way, to a more general principle M(U|H) asserting that H contains a matching covering all vertices in U ⊆ V(H).