It has recently been shown that local search is surprisingly good at finding satisfying assignments for certain classes of CNF formulas [24]. In this paper we demonstrate that the power of local search for satisfiability testing can be further enhanced by employinga new strategy, called "mixed random walk", for escaping from local minima. We present experimental results showing how this strategy allows us to handle formulas that are substantially larger than those that can be solved with basic local search. We also present a detailed comparison of our random walk strategy with simulated annealing. Our results show that mixed random walk is the superior strategy on several classes of computationally difficult problem instances. Finally, we present results demonstrating the effectiveness of local search with walk for solving circuit synthesis and diagnosis problems.

. A greedy randomized adaptive search procedure (Grasp) is a randomized heuristic that has been shown to quickly produce good quality solutions for a wide variety of combinatorial optimization problems. In this paper, we describe a Grasp for the satisfiability (SAT) problem. This algorithm can be also directly applied to both the weighted and unweighted versions of the maximum satisfiability (MAX-SAT) problem. We review basic concepts of Grasp: construction and local search algorithms. The implementation of Grasp for the SAT problem is described in detail. Computational experience on a large set of test problems is presented. Key words. Combinatorial optimization, logic, satisfiability, artificial intelligence, local search, Grasp, computer implementation AMS(MOS) subject classifications. 90B80, 90C20, 90C35, 90C27, 65H20, 65K05 1. Introduction. Let x be a Boolean variable, i.e. a variable that takes on only the values true or false, and let a literal be a variable x or its negation...

this paper we present a simple method for using a local search algorithm to derive

. This paper presents new results on an innovative approach for solving satisfiability problems (SAT), that is, creating a logic circuit that is specialized to solve each problem instance on Field Programmable Gate Arrays (FPGAs). This approach has become feasible due to recent advances in Reconfigurable Computing, and has opened up an exciting new research field in algorithm design. Wehave developed an algorithm that is suitable for a logic circuit implementation. This algorithm is basically equivalent to the Davis-Putnam procedure with Experimental Unit Propagation. The algorithm requires fewer hardware resources than previous approaches. Simulation results show that this method can solve a hard random 3-SAT problem with 400 variables within 1.6 minutes at a clock rate of 10MHz. Faster speeds can be obtained by increasing the clock rate. Furthermore, wehave actually implemented a 128-variable, 256-clause problem instance on FPGAs. 1

This paper reports on an innovative approach for solving satisfiability problems for propositional formulas in conjunctive normal form (SAT) by creating a logic circuit that is specialized to solve each problem instance on field programmable gate arrays (FPGAs). This approach has become feasible due to recent advances in reconfigurable computing and has opened up an exciting new research field in algorithm design. SAT is an important subclass of constraint satisfaction problems, which can formalize a wide range of application problems.

When using the computing resources of workstation networks by parallel programs dynamic load balancing is an important task. We describe a distributed, local migration algorithm, called precomputation-based load balancing, which treats this problem efficiently. Its performance is empirically demonstrated solving the satisfiability problem on an heterogenous network of 12 workstations. We discuss the influence of processor weighting and parameter adaption on speedup and idle times. 1 Introduction Clusters of powerful workstations with high-speed interconnection networks are working at many organisations. Their main task is to supply users with short reply times for interactive work, so typically systems have a low average CPU utilization and high idle times. Thus the computing resources of workstation networks may catch up with the power of mainframes or special-purpose supercomputers. To make these hidden, economical CPU cycles usable to parallel programs, dynamic load balancing is ne...

An algorithm, called PLB is introduced, which redistributes workload in a processor network N in order to supply every processor of N with (about) the same amount of workload. PLB is defined in its basic form for trees, but can be extended to other topologies. The redistribution is done locally on the basis of information of over- or underload in subnetworks of N . We show, that PLB performs O(\Delta) steps, only, where \Delta denotes the diameter of N , and in the average case at most four times as many workload has to be migrated in complete binary trees compared to clique networks, the best possible networks. We describe an implementation of PLB and present experimental results when solving the Boolean satisfiability problem, demonstrating that PLB performs very well in practice.

In our previous research work, we have studied the detection and the use of symmetries to improve the efficiency of satisfiability algorithms. We have believed that the most hard SAT problems present strucural properties such as symmetries. However, recently several authors have exhibited a threshold phenomena on randomly generated K-SAT instances. It is shown that hard instances can be generated randomly using the fixed clause-length model. The experiments we have performed on such instances show that they don't contain symmetries. This fact renews our interest in random generation of SAT instances. More precisely, we focus our research on the two following questions : ffl is it possible to use conditions based on symmetry in random generation in order to generate even harder instances ? ffl we have observed that the instances generated by the fixed clause-length model contains about 50% of horn clauses. What happens if we tend the probability of negating the variables from 0.5 to ...