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36
A Combinatorial Characterization of Resolution Width
 In 18th IEEE Conference on Computational Complexity
, 2002
"... We provide a characterization of the resolution width introduced in the context of propositional proof complexity in terms of the existential pebble game introduced in the context of finite model theory. The characterization is tight and purely combinatorial. Our first application of this result i ..."
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Cited by 52 (6 self)
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We provide a characterization of the resolution width introduced in the context of propositional proof complexity in terms of the existential pebble game introduced in the context of finite model theory. The characterization is tight and purely combinatorial. Our first application of this result is a surprising proof that the minimum space of refuting a 3CNF formula is always bounded from below by the minimum width of refuting it (minus 3). This solves a wellknown open problem. The second application is the unification of several width lower bound arguments, and a new width lower bound for the Dense Linear Order Principle. Since we also show that this principle has resolution refutations of polynomial size, this provides yet another example showing that the sizewidth relationship is tight.
Pseudorandom Generators Hard for kDNF Resolution and Polynomial Calculus. Unpublished
, 2003
"... Abstract A pseudorandom generator Gn : {0, 1} n → {0, 1} m is hard for a propositional proof system P if (roughly speaking) P cannot efficiently prove the statement Gn(x1, . . . , xn) = b for any string b ∈ {0, 1} m . We present a func ) generator which is hard for Res(ε log n); here Res(k) is the ..."
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Abstract A pseudorandom generator Gn : {0, 1} n → {0, 1} m is hard for a propositional proof system P if (roughly speaking) P cannot efficiently prove the statement Gn(x1, . . . , xn) = b for any string b ∈ {0, 1} m . We present a func ) generator which is hard for Res(ε log n); here Res(k) is the propositional proof system that extends Resolution by allowing kDNFs instead of clauses. As a direct consequence of this result, we show that whenever t ≥ n 2 , every Res(ε log t) proof of the principle ¬Circuitt(fn) (asserting that the circuit size of a Boolean function fn in n variables is greater than t) must have size exp(t Ω(1) ). In particular, Res(log log N ) (N ∼ 2 n is the overall number of propositional variables) does not possess efficient proofs of NP ⊆ P/poly. Similar results hold also for the system PCR (the natural common extension of Polynomial Calculus and Resolution) when the characteristic of the ground field is different from 2. As a byproduct, we also improve on the small restriction switching lemma due to Segerlind, Buss and Impagliazzo by removing a square root from the final bound. This in particular implies that the (moderately) weak pigeonhole principle PHP 2n n is hard for Res(ε log n/ log log n).
On the Complexity of Resolution with Bounded Conjunctions
, 2004
"... We analyze size and space complexity of Res(k), a family of propositional proof systems introduced by Krajicek in [20] which extends Resolution by allowing disjunctions of conjunctions of up to k * 1 literals. We show that the treelike Res(k) proof systems form a strict hierarchy with respect to pr ..."
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Cited by 28 (4 self)
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We analyze size and space complexity of Res(k), a family of propositional proof systems introduced by Krajicek in [20] which extends Resolution by allowing disjunctions of conjunctions of up to k * 1 literals. We show that the treelike Res(k) proof systems form a strict hierarchy with respect to proof size and also with respect to space. Moreover Resolution, while simulating treelike Res(k), is almost exponentially separated from treelike Res(k). To study space complexity
Dual weak pigeonhole principle, pseudosurjective functions, and provability of circuit lower bounds
"... ..."
Narrow proofs may be spacious: Separating space and width in resolution (Extended Abstract)
 REVISION 02, ELECTRONIC COLLOQUIUM ON COMPUTATIONAL COMPLEXITY (ECCC
, 2005
"... The width of a resolution proof is the maximal number of literals in any clause of the proof. The space of a proof is the maximal number of clauses kept in memory simultaneously if the proof is only allowed to infer new clauses from clauses currently in memory. Both of these measures have previously ..."
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Cited by 20 (7 self)
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The width of a resolution proof is the maximal number of literals in any clause of the proof. The space of a proof is the maximal number of clauses kept in memory simultaneously if the proof is only allowed to infer new clauses from clauses currently in memory. Both of these measures have previously been studied and related to the resolution refutation size of unsatisfiable CNF formulas. Also, the refutation space of a formula has been proven to be at least as large as the refutation width, but it has been open whether space can be separated from width or the two measures coincide asymptotically. We prove that there is a family of kCNF formulas for which the refutation width in resolution is constant but the refutation space is nonconstant, thus solving a problem mentioned in several previous papers.
Resolution Lower Bounds for the Weak Functional Pigeonhole Principle
 Theoretical Computer Science
, 2002
"... We show that every resolution proof of the functional version FPHP n of the pigeonhole principle (in which one pigeon may not split between several holes) must have size exp . This implies an exp bound when the number of pigeons m is arbitrary. ..."
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We show that every resolution proof of the functional version FPHP n of the pigeonhole principle (in which one pigeon may not split between several holes) must have size exp . This implies an exp bound when the number of pigeons m is arbitrary.
Proof complexity
"... This note, based on my 4ecm lecture, exposes few basic points of proof complexity in a way accessible to any mathematician. ..."
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Cited by 12 (1 self)
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This note, based on my 4ecm lecture, exposes few basic points of proof complexity in a way accessible to any mathematician.
Structure and problem hardness: Goal asymmetry and DPLL proofs in SATbased planning
, 2006
"... In AI Planning, as well as Verification, a successful method is to compile the application into boolean satisfiability (SAT), and solve it with stateoftheart DPLLbased procedures. There is a lack of formal understanding why this works so well. Focussing on the Planning context, we identify a for ..."
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Cited by 11 (5 self)
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In AI Planning, as well as Verification, a successful method is to compile the application into boolean satisfiability (SAT), and solve it with stateoftheart DPLLbased procedures. There is a lack of formal understanding why this works so well. Focussing on the Planning context, we identify a form of problem structure concerned with the symmetrical or asymmetrical nature of the cost of achieving the individual planning goals. We quantify this sort of structure with a simple numeric parameter called AsymRatio, ranging between 0 and 1. We show empirically that AsymRatio correlates strongly with SAT solver performance in a broad range of Planning benchmarks, including the domains used in the 3rd International Planning Competition. We then examine carefully crafted synthetic planning domains that allow to control the amount of structure, and that are clean enough for a rigorous analysis of the combinatorial search space. The domains are parameterized by size n, and by a structure parameter k, so that AsymRatio is asymptotic to k/n. The CNFs we examine are unsatisfiable, encoding one planning step less than the length of the optimal plan. We prove upper and lower bounds on the size of the best possible DPLL refutations, under different settings of k, as a function of n. We also identify the best possible sets of branching variables (backdoors). With minimum AsymRatio, we prove exponential lower bounds, and identify minimal backdoors of size linear in the number of variables. With maximum AsymRatio, we identify logarithmic DPLL refutations (and backdoors), showing a doubly exponential gap between the two structural extreme cases. This provides a concrete insight into the practical efficiency of modern SAT solvers.
Is P versus NP formally independent
 Bulletin of the European Association for Theoretical Computer Science
, 2003
"... I have moved back to the University of Chicago and so has the web page for this column. See above for new URL and contact informaion. This issue Scott Aaronson writes quite an interesting (and opinionated) column on whether the P = NP question is independent of the usual axiom systems. Enjoy! ..."
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I have moved back to the University of Chicago and so has the web page for this column. See above for new URL and contact informaion. This issue Scott Aaronson writes quite an interesting (and opinionated) column on whether the P = NP question is independent of the usual axiom systems. Enjoy!
Proof complexity of pigeonhole principles
 IN PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON DEVELOPMENTS IN LANGUAGE THEORY
, 2002
"... The pigeonhole principle asserts that there is no injective mapping from m pigeons to n holes as long as m> n. It is amazingly simple, expresses one of the most basic primitives in mathematics and Theoretical Computer Science (counting) and, for these reasons, is probably the most extensively stu ..."
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The pigeonhole principle asserts that there is no injective mapping from m pigeons to n holes as long as m> n. It is amazingly simple, expresses one of the most basic primitives in mathematics and Theoretical Computer Science (counting) and, for these reasons, is probably the most extensively studied combinatorial principle. In this survey we try to summarize what is known about its proof complexity, and what we would still like to prove. We also mention some applications of the pigeonhole principle to the study of efficient provability of major open problems in computational complexity, as well as some of its generalizations in the form of general matching principles.