Proof Complexity in Algebraic Systems and Bounded Depth Frege Systems with Modular Counting
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The Deduction Rule and Linear and Near-Linear Proof Simulations
The Deduction Rule and Linear and Near-linear Proof Simulations Maria Luisa Bonet¤ Department of Mathematics University of California, San Diego Samuel R. Buss¤ Department of Mathematics University of California, San Diego Abstract We introduce new proof systems for propositional logic, simple deduction Frege systems, general deduction Frege systems and nested deduction Frege systems, which augment Frege systems with variants of the deduction rule. We give upper bounds on the lengths of proofs in Frege proof systems compared to lengths in these new systems. As applications we give near-linear simulations of the propositional Gentzen sequent calculus and the natural deduction calculus by Frege proofs. The length of a proof is the number of lines (or formulas) in the proof. A general deduction Frege proof system provides at most quadratic speedup over Frege proof systems. A nested deduction Frege proof system provides at most a nearly linear speedup over Frege system where by \nearly linear" is meant the ratio of proof lengths is O(®(n)) where ® is the inverse Ackermann function. A nested deduction Frege system can linearly simulate the propositional sequent calculus, the tree-like general deduction Frege calculus, and the natural deduction calculus. Hence a Frege proof system can simulate all those proof systems with proof lengths bounded by O(n ¢ ®(n)). Also we show ¤Supported in part by NSF Grant DMS-8902480. 1 that a Frege proof of n lines can be transformed into a tree-like Frege proof of O(n log n) lines and of height O(log n). As a corollary of this fact we can prove that natural deduction and sequent calculus tree-like systems simulate Frege systems with proof lengths bounded by O(n log n). -
Circuit Complexity, Proof Complexity and Polynomial Identity Testing
Electronic Colloquium on Computational Complexity, Report No. 52 (2014) Circuit Complexity, Proof Complexity and Polynomial Identity Testing Joshua A. Grochow and Toniann Pitassi April 12, 2014 Abstract We introduce a new and very natural algebraic proof system, which has tight connections to (algebraic) circuit complexity. In particular, we show that any super-polynomial lower bound on any Boolean tautology in our proof system implies that the permanent does not have polynomial- size algebraic circuits (VNP 6= VP). As a corollary to the proof, we also show that super- polynomial lower bounds on the number of lines in Polynomial Calculus proofs (as opposed to the usual measure of number of monomials) imply the Permanent versus Determinant Conjecture. Note that, prior to our work, there was no proof system for which lower bounds on an arbitrary tautology implied any computational lower bound. Our proof system helps clarify the relationships between previous algebraic proof systems, and begins to shed light on why proof complexity lower bounds for various proof systems have been so much harder than lower bounds on the corresponding circuit classes. In doing so, we highlight the importance of polynomial identity testing (PIT) for understanding proof complex- ity. More specifically, we introduce certain propositional axioms satisfied by any Boolean circuit computing PIT. (The existence of efficient proofs for our PIT axioms appears to be somewhere in between the major conjecture that PIT2 P and the known result that PIT2 P=poly.) We use these PIT axioms to shed light on AC0[p]-Frege lower bounds, which have been open for nearly 30 years, with no satisfactory explanation as to their apparent difficulty. -
Reflection Principles, Propositional Proof Systems, and Theories
Reflection principles, propositional proof systems, and theories In memory of Gaisi Takeuti Pavel Pudl´ak ∗ July 30, 2020 Abstract The reflection principle is the statement that if a sentence is provable then it is true. Reflection principles have been studied for first-order theories, but they also play an important role in propositional proof complexity. In this paper we will revisit some results about the reflection principles for propositional proofs systems using a finer scale of reflection principles. We will use the result that proving lower bounds on Resolution proofs is hard in Resolution. This appeared first in the recent article of Atserias and M¨uller [2] as a key lemma and was generalized and simplified in some spin- off papers [11, 13, 12]. We will also survey some results about arithmetical theories and proof systems associated with them. We will show a connection between a conjecture about proof complexity of finite consistency statements and a statement about proof systems associated with a theory. 1 Introduction arXiv:2007.14835v1 [math.LO] 29 Jul 2020 This paper is essentially a survey of some well-known results in proof complexity supple- mented with some observations. In most cases we will also sketch or give an idea of the proofs. Our aim is to focus on some interesting results rather than giving a complete ac- count of known results. We presuppose knowledge of basic concepts and theorems in proof complexity. An excellent source is Kraj´ıˇcek’s last book [19], where you can find the necessary definitions and read more about the results mentioned here. -
Contents Part 1. a Tour of Propositional Proof Complexity. 418 1. Is There A
The Bulletin of Symbolic Logic Volume 13, Number 4, Dec. 2007 THE COMPLEXITY OF PROPOSITIONAL PROOFS NATHAN SEGERLIND Abstract. Propositional proof complexity is the study of the sizes of propositional proofs, and more generally, the resources necessary to certify propositional tautologies. Questions about proof sizes have connections with computational complexity, theories of arithmetic, and satisfiability algorithms. This is article includes a broad survey of the field, and a technical exposition of some recently developed techniques for proving lower bounds on proof sizes. Contents Part 1. A tour of propositional proof complexity. 418 1. Is there a way to prove every tautology with a short proof? 418 2. Satisfiability algorithms and theories of arithmetic 420 3. A menagerie of Frege-like proof systems 427 4. Reverse mathematics of propositional principles 434 5. Feasible interpolation 437 6. Further connections with satisfiability algorithms 438 7. Beyond the Frege systems 441 Part 2. Some lower bounds on refutation sizes. 444 8. The size-width trade-off for resolution 445 9. The small restriction switching lemma 450 10. Expansion clean-up and random 3-CNFs 459 11. Resolution pseudowidth and very weak pigeonhole principles 467 Part 3. Open problems, further reading, acknowledgments. 471 Appendix. Notation. 472 References. 473 Received June 27, 2007. This survey article is based upon the author’s Sacks Prize winning PhD dissertation, however, the emphasis here is on context. The vast majority of results discussed are not the author’s, and several results from the author’s dissertation are omitted. c 2007, Association for Symbolic Logic 1079-8986/07/1304-0001/$7.50 417 418 NATHAN SEGERLIND Part 1. -
A Short History of Computational Complexity
The Computational Complexity Column by Lance FORTNOW NEC Laboratories America 4 Independence Way, Princeton, NJ 08540, USA [email protected] http://www.neci.nj.nec.com/homepages/fortnow/beatcs Every third year the Conference on Computational Complexity is held in Europe and this summer the University of Aarhus (Denmark) will host the meeting July 7-10. More details at the conference web page http://www.computationalcomplexity.org This month we present a historical view of computational complexity written by Steve Homer and myself. This is a preliminary version of a chapter to be included in an upcoming North-Holland Handbook of the History of Mathematical Logic edited by Dirk van Dalen, John Dawson and Aki Kanamori. A Short History of Computational Complexity Lance Fortnow1 Steve Homer2 NEC Research Institute Computer Science Department 4 Independence Way Boston University Princeton, NJ 08540 111 Cummington Street Boston, MA 02215 1 Introduction It all started with a machine. In 1936, Turing developed his theoretical com- putational model. He based his model on how he perceived mathematicians think. As digital computers were developed in the 40's and 50's, the Turing machine proved itself as the right theoretical model for computation. Quickly though we discovered that the basic Turing machine model fails to account for the amount of time or memory needed by a computer, a critical issue today but even more so in those early days of computing. The key idea to measure time and space as a function of the length of the input came in the early 1960's by Hartmanis and Stearns. -
Substitution and Propositional Proof Complexity
Substitution and Propositional Proof Complexity Sam Buss Abstract We discuss substitution rules that allow the substitution of formulas for formula variables. A substitution rule was first introduced by Frege. More recently, substitution is studied in the setting of propositional logic. We state theorems of Urquhart’s giving lower bounds on the number of steps in the substitution Frege system for propositional logic. We give the first superlinear lower bounds on the number of symbols in substitution Frege and multi-substitution Frege proofs. “The length of a proof ought not to be measured by the yard. It is easy to make a proof look short on paper by skipping over many links in the chain of inference and merely indicating large parts of it. Generally people are satisfied if every step in the proof is evidently correct, and this is permissable if one merely wishes to be persuaded that the proposition to be proved is true. But if it is a matter of gaining an insight into the nature of this ‘being evident’, this procedure does not suffice; we must put down all the intermediate steps, that the full light of consciousness may fall upon them.” [G. Frege, Grundgesetze, 1893; translation by M. Furth] 1 Introduction The present article concentrates on the substitution rule allowing the substitution of formulas for formula variables.1 The substitution rule has long pedigree as it was a rule of inference in Frege’s Begriffsschrift [9] and Grundgesetze [10], which contained the first in-depth formalization of logical foundations for mathematics. Since then, substitution has become much less important for the foundations of Sam Buss Department of Mathematics, University of California, San Diego, e-mail: [email protected], Supported in part by Simons Foundation grant 578919. -
On Monotone Circuits with Local Oracles and Clique Lower Bounds
On monotone circuits with local oracles and clique lower bounds Jan Kraj´ıˇcek Igor C. Oliveira Faculty of Mathematics and Physics Charles University in Prague December 17, 2019 Abstract We investigate monotone circuits with local oracles [K., 2016], i.e., circuits containing additional inputs yi = yi(~x) that can perform unstructured computations on the input string ~x. Let µ [0, 1] be the locality of the circuit, a parameter that bounds the combined ∈ m strength of the oracle functions yi(~x), and Un,k, Vn,k 0, 1 be the set of k-cliques and the set of complete (k 1)-partite graphs, respectively⊆ (similarly { } to [Razborov, 1985]). Our results can be informally− stated as follows. (i) For an appropriate extension of depth-2 monotone circuits with local oracles, we show that the size of the smallest circuits separating Un,3 (triangles) and Vn,3 (complete bipartite graphs) undergoes two phase transitions according to µ. (ii) For 5 k(n) n1/4, arbitrary depth, and µ 1/50, we prove that the monotone ≤ ≤ ≤ Θ(√k) circuit size complexity of separating the sets Un,k and Vn,k is n , under a certain restrictive assumption on the local oracle gates. The second result, which concerns monotone circuits with restricted oracles, extends and provides a matching upper bound for the exponential lower bounds on the monotone circuit size complexity of k-clique obtained by Alon and Boppana (1987). 1 Introduction and motivation arXiv:1704.06241v3 [cs.CC] 16 Dec 2019 We establish initial lower bounds on the power of monotone circuits with local oracles (mono- tone CLOs), an extension of monotone circuits introduced in [Kra16] motivated by problems in proof complexity. -