Variable ordering heuristics are used to reduce the cost of searching for a solution to a constraint satisfaction problem. On real problems that have non-binary and non-uniform constraints it is harder to make the optimal choice of variable ordering because surprisingly little is known about when and why variable ordering heuristics perform well. In an attempt to address this problem we present...

We present a new and more efficient heuristic by restricting lookahead saturation (LAS) with NVO (neighbourhood variable ordering) and DEW (dynamic equality weighting). We report on the integration of this heuristic in Satz, a high-performance SAT solver, showing empirically that it significantly improves the performance on an extensive range of benchmark problems that exhibit hard structure.

the internal structure of dna-ctab and dna-ddab is investigated by a small angel x- ray scattering(saxs) instrument. hexagonal packing of dna was observed for dna complex with ctab, and for ddabcomplex is observed lamellar structure. variations in the internal spacing and degree of long-range ordering aredependent on both surfactant type and concentrations of added salt. when we increased the a...

This paper present two variable ordering methods for minimizing Reduced Ordered Binary Decision Diagrams (ROBDDs). The broad formal techniques, based on graph topology, and the details of the algorithms are suitable for further work on other algorithms of the same family. Experiments have shown that the algorithms improve on common algorithms for the majority of benchmark circuits.

In this paper we present and evaluate an evolutionary approach for learning new constraint satisfaction algorithms, specifically for MAX-SAT optimisation problems. Our approach offers two significant advantages over existing methods: it allows the evolution of more complex combinations of heuristics, and; it can identify fruitful synergies among heuristics. Using four different classes of MAX-S...

It is well known that the order in which variables are instantiated by a backtracking search algorithm can make an enormous difference to the search effort in solving CSPs. Among the plethora of heuristics that have been proposed in the literature to efficiently order variables during search, a significant recently proposed class uses the learning-from-failure approach. Prime examples of such h...

In Bayesian networks, amost probable explanation (MPE) is a most likely instantiation of all network variables given a piece of evidence. Recent work proposed a branch-and-boundsearch algorithm that finds exact solutions to MPE queries, where bounds are computed on a relaxed network obtained by a technique known as node splitting. In this work we study the impact of variable and value ordering ...