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20022026
most citedResolution Trees with Lemmas: Resolution Refinements that Characterize DLL Algorithms with Clause Learning

47 citations · 92 across the 12 of their papers we have counts for

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8 papers · 1 filter

cs.LO2024

Extended Resolution Clause Learning via Dual Implication Points

Sam Buss, Jonathan Chung, Vijay Ganesh +1

We present a new extended resolution clause learning (ERCL) algorithm, implemented as part of a conflict-driven clause-learning (CDCL) SAT solver, wherein new variables are dynamic…

cs.LO2024

Regular resolution effectively simulates resolution

Sam Buss, Emre Yolcu

Regular resolution is a refinement of the resolution proof system requiring that no variable be resolved on more than once along any path in the proof. It is known that there exist…

cs.LO2014★ 2 cited

Sub-computable Boundedness Randomness

Sam Buss, Douglas Cenzer, Jeffrey B. Remmel

This paper defines a new notion of bounded computable randomness for certain classes of sub-computable functions which lack a universal machine. In particular, we define such versi…

cs.LO2014★ 40 cited

Small Stone in Pool

Samuel R. Buss, Leszek Aleksander Kolodziejczyk

The Stone tautologies are known to have polynomial size resolution refutations and require exponential size regular refutations. We prove that the Stone tautologies also have polyn…

cs.LO2012★ 1 cited

Improved Separations of Regular Resolution from Clause Learning Proof Systems

Maria Luisa Bonet, Sam Buss, Jan Johannsen

We prove that the graph tautology formulas of Alekhnovich, Johannsen, Pitassi, and Urquhart have polynomial size pool resolution refutations that use only input lemmas as learned c…

cs.LO2012

An Improved Separation of Regular Resolution from Pool Resolution and Clause Learning

Maria Luisa Bonet, Sam Buss

We prove that the graph tautology principles of Alekhnovich, Johannsen, Pitassi and Urquhart have polynomial size pool resolution refutations that use only input lemmas as learned…