Abstract. In this paper, we explore the two well-known principles of diversification and intensification in portfolio-based parallel SAT solving. These dual concepts play an important role in several search algorithms including local search, and appear to be a key point in modern parallel SAT solvers. To study their tradeoff, we define two roles for the computational units. Some of them classified as Masters perform an original search strategy, ensuring diversification. The remaining units, classified as Slaves are there to intensify their master's strategy. Several important questions have to be answered. The first one is what information should be given to a slave in order to intensify a given search effort? The second one is, how often, a subordinated unit has to receive such information? Finally, the question of finding the number of subordinated units along their connections with the search efforts has to be answered. Our results lead to an original intensification strategy which outperforms the best parallel SAT solver, and solves some open SAT instances.

Conflict based clause learning is known to be an important component in Modern SAT solving. Because of the exponential blow up of the size of learnt clauses database, maintaining a relevant and polynomially bounded set of learnt clauses is crucial for the efficiency of clause learning based SAT solvers. In this paper, we first compare several criteria for selecting the most relevant learnt clauses with a simple random selection strategy. We then propose new criteria allowing us to select relevant clauses w.r.t. a given search state. Then, we use such strategies as a means to diversify the search in a portfolio based parallel solver. An experimental evaluation comparing the classical ManySAT solver with the one augmented with multiple deletion strategies, shows the interest of such approach.

The digital measurement of free-form surface is the key to machining quality inspection for surface parts, and the basic requirement of digital measurement is how to realize the adaptive distribution of sampling points with the curvature feature. The traditional sampling methods were limited to the surface known mathematical model. This paper was concerned with free-form surface sampling with CAD model. Firstly, an algorithm of extracting data points from free-form surface was proposed, which transformed the free-from surface into intersection lines. Secondly, the B-spline interpolation was utilized to acquire the parameter expression of each line. Then the curvature measure function was defined on the basis of the curvature and the spline mass was determined taking the curvature measure as density. A new sampling method was presented based on dividing the spline mass equally. On this basis, the sampling process for surface was formulated, which realized the adaptive distribution for surface based on CAD model. The computer simulations show that the suggested method can approximate the curves and surfaces with higher precision comparing against other sampling methods.

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