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Efthimios G. Lalas

Bio: Efthimios G. Lalas is an academic researcher from University of Patras. The author has contributed to research in topics: Satisfiability & Literal (mathematical logic). The author has an hindex of 3, co-authored 4 publications receiving 296 citations.

Papers
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Book ChapterDOI
17 Sep 2002
TL;DR: It is proved that for c < 3.42 a slight modification of this algorithm computes a satisfying truth assignment with probability asymptotically bounded away from zero.
Abstract: Consider the following simple, greedy Davis-Putnam algorithm applied to a random 3-CNF formula of constant density c: Arbitrarily set to TRUE a literal that appears in as many clauses as possible, irrespective of their size (and irrespective of the number of occurrences of the negation of the literal). Reduce the formula. If any unit clauses appear, then satisfy their literals arbitrarily, reducing the formula accordingly, until no unit clause remains. Repeat. We prove that for c < 3.42 a slight modification of this algorithm computes a satisfying truth assignment with probability asymptotically bounded away from zero. Previously, algorithms of increasing sophistication were shown to succeed for c < 3.26. Preliminary experiments we performed suggest that c ? 3.6 is feasible running algorithms like the above, which take into account not only the number of occurrences of a literal but also the number of occurrences of its negation, irrespectively of clause-size information.

145 citations

Journal IssueDOI
TL;DR: It is proved that for r3 < 3.42 this heuristic succeeds with probability asymptotically bounded away from zero, and improves up to r3 > 3.52 by further exploiting the degree of the negation of the evaluated to True literal.
Abstract: On input a random 3-CNF formula of clauses-to-variables ratio r3 applies repeatedly the following simple heuristic: Set to True a literal that appears in the maximum number of clauses, irrespective of their size and the number of occurrences of the negation of the literal (ties are broken randomly; 1-clauses when they appear get priority) We prove that for r3 < 342 this heuristic succeeds with probability asymptotically bounded away from zero Previously, heuristics of increasing sophistication were shown to succeed for r3 < 326 We improve up to r3 < 352 by further exploiting the degree of the negation of the evaluated to True literal © 2005 Wiley Periodicals, Inc Random Struct Alg, 2006

85 citations

Journal ArticleDOI
TL;DR: It is proved that the heuristic asymptotically certainly succeeds in producing a satisfying truth assignment for formulas with clauses to variables ratio (density) of up to 3.52, and not only the satisfiability threshold, but also the threshold where the complexity of searching for satisfying truth assignments jumps from polynomial to exponential is at least3.52.

66 citations

Journal Article
TL;DR: In this article, a simple greedy Davis-Putnam algorithm is applied to a random 3-CNF formula of constant density c: Arbitrarily set to TRUE a literal that appears in as many clauses as possible, irrespective of their size, and irrespective of the number of occurrences of the negation of the literal.
Abstract: Consider the following simple, greedy Davis-Putnam algorithm applied to a random 3-CNF formula of constant density c: Arbitrarily set to TRUE a literal that appears in as many clauses as possible, irrespective of their size (and irrespective of the number of occurrences of the negation of the literal). Reduce the formula. If any unit clauses appear, then satisfy their literals arbitrarily, reducing the formula accordingly, until no unit clause remains. Repeat. We prove that for c < 3.42 a slight modification of this algorithm computes a satisfying truth assignment with probability asymptotically bounded away from zero. Previously, algorithms of increasing sophistication were shown to succeed for c < 3.26. Preliminary experiments we performed suggest that c ≃ 3.6 is feasible running algorithms like the above, which take into account not only the number of occurrences of a literal but also the number of occurrences of its negation, irrespectively of clause-size information.

5 citations


Cited by
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Book
08 Jan 2008
TL;DR: The Handbook of Knowledge Representation is an up-to-date review of twenty-five key topics in knowledge representation written by the leaders of each field, an essential resource for students, researchers and practitioners in all areas of Artificial Intelligence.
Abstract: Knowledge Representation, which lies at the core of Artificial Intelligence, is concerned with encoding knowledge on computers to enable systems to reason automatically. The Handbook of Knowledge Representation is an up-to-date review of twenty-five key topics in knowledge representation, written by the leaders of each field.This book is an essential resource for students, researchers and practitioners in all areas of Artificial Intelligence. * Make your computer smarter* Handle qualitative and uncertain information* Improve computational tractability to solve your problems easily

785 citations

Book
01 Mar 2021
TL;DR: A collection of papers on all theoretical and practical aspects of SAT solving will be extremely useful to both students and researchers and will lead to many further advances in the field.
Abstract: 'Satisfiability (SAT) related topics have attracted researchers from various disciplines: logic, applied areas such as planning, scheduling, operations research and combinatorial optimization, but also theoretical issues on the theme of complexity and much more, they all are connected through SAT. My personal interest in SAT stems from actual solving: The increase in power of modern SAT solvers over the past 15 years has been phenomenal. It has become the key enabling technology in automated verification of both computer hardware and software' - Edmund M. Clarke (FORE Systems University Professor of Computer Science and Professor of Electrical and Computer Engineering at Carnegie Mellon University). 'Bounded Model Checking (BMC) of computer hardware is now probably the most widely used model checking technique. The counterexamples that it finds are just satisfying instances of a Boolean formula obtained by unwinding to some fixed depth a sequential circuit and its specification in linear temporal logic. Extending model checking to software verification is a much more difficult problem on the frontier of current research. One promising approach for languages like C with finite word-length integers is to use the same idea as in BMC but with a decision procedure for the theory of bit-vectors instead of SAT. All decision procedures for bit-vectors that I am familiar with ultimately make use of a fast SAT solver to handle complex formulas' - Edmund M. Clarke (FORE Systems University Professor of Computer Science and Professor of Electrical and Computer Engineering at Carnegie Mellon University). 'Decision procedures for more complicated theories, like linear real and integer arithmetic, are also used in program verification. Most of them use powerful SAT solvers in an essential way. Clearly, efficient SAT solving is a key technology for 21st century computer science. I expect this collection of papers on all theoretical and practical aspects of SAT solving will be extremely useful to both students and researchers and will lead to many further advances in the field' - Edmund M. Clarke (FORE Systems University Professor of Computer Science and Professor of Electrical and Computer Engineering at Carnegie Mellon University).

633 citations

Posted Content
TL;DR: A new type of message passing algorithm is introduced which allows to find efficiently a satisfying assignment of the variables in this difficult region of randomly generated formulas.
Abstract: We study the satisfiability of randomly generated formulas formed by $M$ clauses of exactly $K$ literals over $N$ Boolean variables. For a given value of $N$ the problem is known to be most difficult with $\alpha=M/N$ close to the experimental threshold $\alpha_c$ separating the region where almost all formulas are SAT from the region where all formulas are UNSAT. Recent results from a statistical physics analysis suggest that the difficulty is related to the existence of a clustering phenomenon of the solutions when $\alpha$ is close to (but smaller than) $\alpha_c$. We introduce a new type of message passing algorithm which allows to find efficiently a satisfiable assignment of the variables in the difficult region. This algorithm is iterative and composed of two main parts. The first is a message-passing procedure which generalizes the usual methods like Sum-Product or Belief Propagation: it passes messages that are surveys over clusters of the ordinary messages. The second part uses the detailed probabilistic information obtained from the surveys in order to fix variables and simplify the problem. Eventually, the simplified problem that remains is solved by a conventional heuristic.

369 citations

Journal ArticleDOI
09 Jun 2005-Nature
TL;DR: The results prove that the heuristic predictions of statistical physics in this context are essentially correct and establish that random instances of constraint satisfaction problems have solutions well beyond the reach of any analysed algorithm.
Abstract: It is widely believed that for many optimization problems, no algorithm is substantially more efficient than exhaustive search. This means that finding optimal solutions for many practical problems is completely beyond any current or projected computational capacity. To understand the origin of this extreme 'hardness', computer scientists, mathematicians and physicists have been investigating for two decades a connection between computational complexity and phase transitions in random instances of constraint satisfaction problems. Here we present a mathematically rigorous method for locating such phase transitions. Our method works by analysing the distribution of distances between pairs of solutions as constraints are added. By identifying critical behaviour in the evolution of this distribution, we can pinpoint the threshold location for a number of problems, including the two most-studied ones: random k-SAT and random graph colouring. Our results prove that the heuristic predictions of statistical physics in this context are essentially correct. Moreover, we establish that random instances of constraint satisfaction problems have solutions well beyond the reach of any analysed algorithm.

258 citations

Book ChapterDOI
01 Jan 2008
TL;DR: This chapter describes the main solution techniques used in modern SAT solvers, classifying them as complete and incomplete methods, and presents several extensions of the SAT approach currently under development.
Abstract: Publisher Summary The past few years have seen enormous progress in the performance of Boolean satisfiability (SAT) solvers. Despite the worst-case exponential run time of all known algorithms, satisfiability solvers are increasingly leaving their mark as a general-purpose tool in areas as diverse as software and hardware verification, automatic test-pattern generation, planning, scheduling, and even challenging problems from algebra. Annual SAT competitions have led to the development of dozens of clever implementations of such solvers, exploration of new techniques, and creation of an extensive suite of real-world instances as well as challenging hand-crafted benchmark problems. Modern SAT solvers provide a black-box procedure that can often solve hard structured problems with over a million variables and several million constraints. This chapter describes the main solution techniques used in modern SAT solvers, classifying them as complete and incomplete methods. It discusses recent insights explaining the effectiveness of these techniques on practical SAT encodings and presents several extensions of the SAT approach currently under development. These extensions further expand the range of applications to include multiagent and probabilistic reasoning.

249 citations