A History of Satisfiability
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“The Church-Turing “Thesis” As a Special Corollary of Gödel's
“The Church-Turing “Thesis” as a Special Corollary of Gödel’s Completeness Theorem,” in Computability: Turing, Gödel, Church, and Beyond, B. J. Copeland, C. Posy, and O. Shagrir (eds.), MIT Press (Cambridge), 2013, pp. 77-104. Saul A. Kripke This is the published version of the book chapter indicated above, which can be obtained from the publisher at https://mitpress.mit.edu/books/computability. It is reproduced here by permission of the publisher who holds the copyright. © The MIT Press The Church-Turing “ Thesis ” as a Special Corollary of G ö del ’ s 4 Completeness Theorem 1 Saul A. Kripke Traditionally, many writers, following Kleene (1952) , thought of the Church-Turing thesis as unprovable by its nature but having various strong arguments in its favor, including Turing ’ s analysis of human computation. More recently, the beauty, power, and obvious fundamental importance of this analysis — what Turing (1936) calls “ argument I ” — has led some writers to give an almost exclusive emphasis on this argument as the unique justification for the Church-Turing thesis. In this chapter I advocate an alternative justification, essentially presupposed by Turing himself in what he calls “ argument II. ” The idea is that computation is a special form of math- ematical deduction. Assuming the steps of the deduction can be stated in a first- order language, the Church-Turing thesis follows as a special case of G ö del ’ s completeness theorem (first-order algorithm theorem). I propose this idea as an alternative foundation for the Church-Turing thesis, both for human and machine computation. Clearly the relevant assumptions are justified for computations pres- ently known. -
Rounding Algorithms for Covering Problems
Mathematical Programming 80 (1998) 63 89 Rounding algorithms for covering problems Dimitris Bertsimas a,,,1, Rakesh Vohra b,2 a Massachusetts Institute of Technology, Sloan School of Management, 50 Memorial Drive, Cambridge, MA 02142-1347, USA b Department of Management Science, Ohio State University, Ohio, USA Received 1 February 1994; received in revised form 1 January 1996 Abstract In the last 25 years approximation algorithms for discrete optimization problems have been in the center of research in the fields of mathematical programming and computer science. Re- cent results from computer science have identified barriers to the degree of approximability of discrete optimization problems unless P -- NP. As a result, as far as negative results are con- cerned a unifying picture is emerging. On the other hand, as far as particular approximation algorithms for different problems are concerned, the picture is not very clear. Different algo- rithms work for different problems and the insights gained from a successful analysis of a par- ticular problem rarely transfer to another. Our goal in this paper is to present a framework for the approximation of a class of integer programming problems (covering problems) through generic heuristics all based on rounding (deterministic using primal and dual information or randomized but with nonlinear rounding functions) of the optimal solution of a linear programming (LP) relaxation. We apply these generic heuristics to obtain in a systematic way many known as well as new results for the set covering, facility location, general covering, network design and cut covering problems. © 1998 The Mathematical Programming Society, Inc. Published by Elsevier Science B.V. -
Structural Graph Theory Meets Algorithms: Covering And
Structural Graph Theory Meets Algorithms: Covering and Connectivity Problems in Graphs Saeed Akhoondian Amiri Fakult¨atIV { Elektrotechnik und Informatik der Technischen Universit¨atBerlin zur Erlangung des akademischen Grades Doktor der Naturwissenschaften Dr. rer. nat. genehmigte Dissertation Promotionsausschuss: Vorsitzender: Prof. Dr. Rolf Niedermeier Gutachter: Prof. Dr. Stephan Kreutzer Gutachter: Prof. Dr. Marcin Pilipczuk Gutachter: Prof. Dr. Dimitrios Thilikos Tag der wissenschaftlichen Aussprache: 13. October 2017 Berlin 2017 2 This thesis is dedicated to my family, especially to my beautiful wife Atefe and my lovely son Shervin. 3 Contents Abstract iii Acknowledgementsv I. Introduction and Preliminaries1 1. Introduction2 1.0.1. General Techniques and Models......................3 1.1. Covering Problems.................................6 1.1.1. Covering Problems in Distributed Models: Case of Dominating Sets.6 1.1.2. Covering Problems in Directed Graphs: Finding Similar Patterns, the Case of Erd}os-P´osaproperty.......................9 1.2. Routing Problems in Directed Graphs...................... 11 1.2.1. Routing Problems............................. 11 1.2.2. Rerouting Problems............................ 12 1.3. Structure of the Thesis and Declaration of Authorship............. 14 2. Preliminaries and Notations 16 2.1. Basic Notations and Defnitions.......................... 16 2.1.1. Sets..................................... 16 2.1.2. Graphs................................... 16 2.2. Complexity Classes................................ -
Solving SAT and SAT Modulo Theories: from an Abstract Davis–Putnam–Logemann–Loveland Procedure to DPLL(T)
Solving SAT and SAT Modulo Theories: From an Abstract Davis–Putnam–Logemann–Loveland Procedure to DPLL(T) ROBERT NIEUWENHUIS AND ALBERT OLIVERAS Technical University of Catalonia, Barcelona, Spain AND CESARE TINELLI The University of Iowa, Iowa City, Iowa Abstract. We first introduce Abstract DPLL, a rule-based formulation of the Davis–Putnam– Logemann–Loveland (DPLL) procedure for propositional satisfiability. This abstract framework al- lows one to cleanly express practical DPLL algorithms and to formally reason about them in a simple way. Its properties, such as soundness, completeness or termination, immediately carry over to the modern DPLL implementations with features such as backjumping or clause learning. We then extend the framework to Satisfiability Modulo background Theories (SMT) and use it to model several variants of the so-called lazy approach for SMT. In particular, we use it to introduce a few variants of a new, efficient and modular approach for SMT based on a general DPLL(X) engine, whose parameter X can be instantiated with a specialized solver SolverT for a given theory T , thus producing a DPLL(T ) system. We describe the high-level design of DPLL(X) and its cooperation with SolverT , discuss the role of theory propagation, and describe different DPLL(T ) strategies for some theories arising in industrial applications. Our extensive experimental evidence, summarized in this article, shows that DPLL(T ) systems can significantly outperform the other state-of-the-art tools, frequently even in orders of magnitude, and have -
3.1 Matchings and Factors: Matchings and Covers
1 3.1 Matchings and Factors: Matchings and Covers This copyrighted material is taken from Introduction to Graph Theory, 2nd Ed., by Doug West; and is not for further distribution beyond this course. These slides will be stored in a limited-access location on an IIT server and are not for distribution or use beyond Math 454/553. 2 Matchings 3.1.1 Definition A matching in a graph G is a set of non-loop edges with no shared endpoints. The vertices incident to the edges of a matching M are saturated by M (M-saturated); the others are unsaturated (M-unsaturated). A perfect matching in a graph is a matching that saturates every vertex. perfect matching M-unsaturated M-saturated M Contains copyrighted material from Introduction to Graph Theory by Doug West, 2nd Ed. Not for distribution beyond IIT’s Math 454/553. 3 Perfect Matchings in Complete Bipartite Graphs a 1 The perfect matchings in a complete b 2 X,Y-bigraph with |X|=|Y| exactly c 3 correspond to the bijections d 4 f: X -> Y e 5 Therefore Kn,n has n! perfect f 6 matchings. g 7 Kn,n The complete graph Kn has a perfect matching iff… Contains copyrighted material from Introduction to Graph Theory by Doug West, 2nd Ed. Not for distribution beyond IIT’s Math 454/553. 4 Perfect Matchings in Complete Graphs The complete graph Kn has a perfect matching iff n is even. So instead of Kn consider K2n. We count the perfect matchings in K2n by: (1) Selecting a vertex v (e.g., with the highest label) one choice u v (2) Selecting a vertex u to match to v K2n-2 2n-1 choices (3) Selecting a perfect matching on the rest of the vertices. -
MASTER THESIS Strong Proof Systems
Charles University in Prague Faculty of Mathematics and Physics MASTER THESIS Ondrej Mikle-Bar´at Strong Proof Systems Department of Software Engineering Supervisor: Prof. RNDr. Jan Kraj´ıˇcek, DrSc. Study program: Computer Science, Software Systems I would like to thank my advisor for his valuable comments and suggestions. His extensive experience with proof systems and logic helped me a lot. Finally I want to thank my family for their support. Prohlaˇsuji, ˇzejsem svou diplomovou pr´acinapsal samostatnˇea v´yhradnˇe s pouˇzit´ımcitovan´ych pramen˚u.Souhlas´ımse zap˚ujˇcov´an´ımpr´ace. I hereby declare that I have created this master thesis on my own and listed all used references. I agree with lending of this thesis. Prague August 23, 2007 Ondrej Mikle-Bar´at Contents 1 Preliminaries 1 1.1 Propositional logic . 1 1.2 Proof complexity . 2 1.3 Resolution . 4 1.3.1 Pigeonhole principle - PHPn ............... 6 2 OBDD proof system 8 2.1 OBDD . 8 2.2 Inference rules . 10 2.3 Strength of OBDD proofs . 11 3 R-OBDD 12 3.1 Motivation . 12 3.2 Definitions . 12 3.3 Inference rules . 13 3.4 The proof system . 13 4 Automated theorem proving in R-OBDD 16 4.1 R-OBDD solver – DPLL modification for R-OBDD . 17 4.2 Discussion . 22 ii N´azevpr´ace:Siln´ed˚ukazov´esyst´emy Autor: Ondrej Mikle-Bar´at Katedra (´ustav): Katedra softwarov´ehoinˇzen´yrstv´ı Vedouc´ıdiplomov´epr´ace: Prof. RNDr. Jan Kraj´ıˇcek, DrSc. e-mail vedouc´ıho:[email protected] Abstrakt: R-OBDD je nov´yCook-Reckhow˚uv d˚ukazov´ysyst´em pro v´yrokovou logiku zaloˇzenna kombinaci OBDD d˚ukazov´ehosyst´emu a rezoluˇcn´ıho d˚ukazov´eho syst´emu. -
Logic and Proof
Logic and Proof Computer Science Tripos Part IB Lent Term Lawrence C Paulson Computer Laboratory University of Cambridge [email protected] Copyright c 2018 by Lawrence C. Paulson I Logic and Proof 101 Introduction to Logic Logic concerns statements in some language. The language can be natural (English, Latin, . ) or formal. Some statements are true, others false or meaningless. Logic concerns relationships between statements: consistency, entailment, . Logical proofs model human reasoning (supposedly). Lawrence C. Paulson University of Cambridge I Logic and Proof 102 Statements Statements are declarative assertions: Black is the colour of my true love’s hair. They are not greetings, questions or commands: What is the colour of my true love’s hair? I wish my true love had hair. Get a haircut! Lawrence C. Paulson University of Cambridge I Logic and Proof 103 Schematic Statements Now let the variables X, Y, Z, . range over ‘real’ objects Black is the colour of X’s hair. Black is the colour of Y. Z is the colour of Y. Schematic statements can even express questions: What things are black? Lawrence C. Paulson University of Cambridge I Logic and Proof 104 Interpretations and Validity An interpretation maps variables to real objects: The interpretation Y 7 coal satisfies the statement Black is the colour→ of Y. but the interpretation Y 7 strawberries does not! A statement A is valid if→ all interpretations satisfy A. Lawrence C. Paulson University of Cambridge I Logic and Proof 105 Consistency, or Satisfiability A set S of statements is consistent if some interpretation satisfies all elements of S at the same time. -
Satisfiability 6 the Decision Problem 7
Satisfiability Difficult Problems Dealing with SAT Implementation Klaus Sutner Carnegie Mellon University 2020/02/04 Finding Hard Problems 2 Entscheidungsproblem to the Rescue 3 The Entscheidungsproblem is solved when one knows a pro- cedure by which one can decide in a finite number of operations So where would be look for hard problems, something that is eminently whether a given logical expression is generally valid or is satis- decidable but appears to be outside of P? And, we’d like the problem to fiable. The solution of the Entscheidungsproblem is of funda- be practical, not some monster from CRT. mental importance for the theory of all fields, the theorems of which are at all capable of logical development from finitely many The Circuit Value Problem is a good indicator for the right direction: axioms. evaluating Boolean expressions is polynomial time, but relatively difficult D. Hilbert within P. So can we push CVP a little bit to force it outside of P, just a little bit? In a sense, [the Entscheidungsproblem] is the most general Say, up into EXP1? problem of mathematics. J. Herbrand Exploiting Difficulty 4 Scaling Back 5 Herbrand is right, of course. As a research project this sounds like a Taking a clue from CVP, how about asking questions about Boolean fiasco. formulae, rather than first-order? But: we can turn adversity into an asset, and use (some version of) the Probably the most natural question that comes to mind here is Entscheidungsproblem as the epitome of a hard problem. Is ϕ(x1, . , xn) a tautology? The original Entscheidungsproblem would presumable have included arbitrary first-order questions about number theory. -
Solving the Boolean Satisfiability Problem Using the Parallel Paradigm Jury Composition
Philosophæ doctor thesis Hoessen Benoît Solving the Boolean Satisfiability problem using the parallel paradigm Jury composition: PhD director Audemard Gilles Professor at Universit´ed'Artois PhD co-director Jabbour Sa¨ıd Assistant Professor at Universit´ed'Artois PhD co-director Piette C´edric Assistant Professor at Universit´ed'Artois Examiner Simon Laurent Professor at University of Bordeaux Examiner Dequen Gilles Professor at University of Picardie Jules Vernes Katsirelos George Charg´ede recherche at Institut national de la recherche agronomique, Toulouse Abstract This thesis presents different technique to solve the Boolean satisfiability problem using parallel and distributed architec- tures. In order to provide a complete explanation, a careful presentation of the CDCL algorithm is made, followed by the state of the art in this domain. Once presented, two proposi- tions are made. The first one is an improvement on a portfo- lio algorithm, allowing to exchange more data without loosing efficiency. The second is a complete library with its API al- lowing to easily create distributed SAT solver. Keywords: SAT, parallelism, distributed, solver, logic R´esum´e Cette th`ese pr´esente diff´erentes techniques permettant de r´esoudre le probl`eme de satisfaction de formule bool´eenes utilisant le parall´elismeet du calcul distribu´e. Dans le but de fournir une explication la plus compl`ete possible, une pr´esentation d´etaill´ee de l'algorithme CDCL est effectu´ee, suivi d'un ´etatde l'art. De ce point de d´epart,deux pistes sont explor´ees. La premi`ereest une am´eliorationd'un algorithme de type portfolio, permettant d'´echanger plus d'informations sans perte d’efficacit´e. -
Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems
Journal of Artificial Intelligence Research 28 (2007) 393-429 Submitted 6/06; published 3/07 Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems Alex S. Fukunaga [email protected] Jet Propulsion Laboratory California Institute of Technology 4800 Oak Grove Drive Pasadena, CA 91108 USA Richard E. Korf [email protected] Computer Science Department University of California, Los Angeles Los Angeles, CA 90095 Abstract Many combinatorial optimization problems such as the bin packing and multiple knap- sack problems involve assigning a set of discrete objects to multiple containers. These prob- lems can be used to model task and resource allocation problems in multi-agent systems and distributed systms, and can also be found as subproblems of scheduling problems. We propose bin completion, a branch-and-bound strategy for one-dimensional, multicontainer packing problems. Bin completion combines a bin-oriented search space with a powerful dominance criterion that enables us to prune much of the space. The performance of the basic bin completion framework can be enhanced by using a number of extensions, in- cluding nogood-based pruning techniques that allow further exploitation of the dominance criterion. Bin completion is applied to four problems: multiple knapsack, bin covering, min-cost covering, and bin packing. We show that our bin completion algorithms yield new, state-of-the-art results for the multiple knapsack, bin covering, and min-cost cov- ering problems, outperforming previous algorithms by several orders of magnitude with respect to runtime on some classes of hard, random problem instances. For the bin pack- ing problem, we demonstrate significant improvements compared to most previous results, but show that bin completion is not competitive with current state-of-the-art cutting-stock based approaches. -
Solving Packing Problems with Few Small Items Using Rainbow Matchings
Solving Packing Problems with Few Small Items Using Rainbow Matchings Max Bannach Institute for Theoretical Computer Science, Universität zu Lübeck, Lübeck, Germany [email protected] Sebastian Berndt Institute for IT Security, Universität zu Lübeck, Lübeck, Germany [email protected] Marten Maack Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Matthias Mnich Institut für Algorithmen und Komplexität, TU Hamburg, Hamburg, Germany [email protected] Alexandra Lassota Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Malin Rau Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, LIG, 38000 Grenoble, France [email protected] Malte Skambath Department of Computer Science, Universität Kiel, Kiel, Germany [email protected] Abstract An important area of combinatorial optimization is the study of packing and covering problems, such as Bin Packing, Multiple Knapsack, and Bin Covering. Those problems have been studied extensively from the viewpoint of approximation algorithms, but their parameterized complexity has only been investigated barely. For problem instances containing no “small” items, classical matching algorithms yield optimal solutions in polynomial time. In this paper we approach them by their distance from triviality, measuring the problem complexity by the number k of small items. Our main results are fixed-parameter algorithms for vector versions of Bin Packing, Multiple Knapsack, and Bin Covering parameterized by k. The algorithms are randomized with one-sided error and run in time 4k · k! · nO(1). To achieve this, we introduce a colored matching problem to which we reduce all these packing problems. The colored matching problem is natural in itself and we expect it to be useful for other applications. -
Automated and Interactive Theorem Proving 1: Background & Propositional Logic
Automated and Interactive Theorem Proving 1: Background & Propositional Logic John Harrison Intel Corporation Marktoberdorf 2007 Thu 2nd August 2007 (08:30–09:15) 0 What I will talk about Aim is to cover some of the most important approaches to computer-aided proof in classical logic. 1. Background and propositional logic 2. First-order logic, with and without equality 3. Decidable problems in logic and algebra 4. Combination and certification of decision procedures 5. Interactive theorem proving 1 What I won’t talk about • Decision procedures for temporal logic, model checking (well covered in other courses) • Higher-order logic (my own interest but off the main topic; will see some of this in other courses) • Undecidability and incompleteness (I don’t have enough time) • Methods for constructive logic, modal logic, other nonclassical logics (I don’t know much anyway) 2 A practical slant Our approach to logic will be highly constructive! Most of what is described is implemented by explicit code that can be obtained here: http://www.cl.cam.ac.uk/users/jrh/atp/ See also my interactive higher-order logic prover HOL Light: http://www.cl.cam.ac.uk/users/jrh/hol-light/ which incorporates many decision procedures in a certified way. 3 Propositional Logic We probably all know what propositional logic is. English Standard Boolean Other false ⊥ 0 F true ⊤ 1 T not p ¬p p −p, ∼ p p and q p ∧ q pq p&q, p · q p or q p ∨ q p + q p | q, por q p implies q p ⇒ q p ≤ q p → q, p ⊃ q p iff q p ⇔ q p = q p ≡ q, p ∼ q In the context of circuits, it’s often referred to as ‘Boolean algebra’, and many designers use the Boolean notation.