Bounded Variable Elimination (BVE) is an important Boolean formula simplification technique in which the variable ordering is crucial. We define a new variable ordering based on variable activity, called ESA (variable Elimination Scheduled by Activity), for in-processing BVE in Conflict-Driven Clause Learning (CDCL) SAT solvers, and incorporate it into several state-of-the-art CDCL SAT solvers. Experimental results show that the new ESA ordering consistently makes these solvers solve more instances on the benchmark set including all the 5675 instances used in the Crafted, Application and Main tracks of all SAT Competitions up to 2022. In particular, one of these solvers with ESA, Kissat_MAB_ESA, won the Anniversary track of the SAT Competition 2022. The behaviour of ESA and the reason of its effectiveness are also analyzed.

MOTIVATION: With the development and the gradually popularized application of next-generation sequencing technologies (NGS), genome sequencing has been becoming faster and cheaper, creating a massive amount of genome sequence data which still grows at an explosive rate. The time and cost of transmission, storage, processing and analysis of these genetic data have become bottlenecks that hinder the development of genetics and biomedicine. Although there are many common data compression algorithms, they are not effective for genome sequences due to their inability to consider and exploit the inherent characteristics of genome sequence data. Therefore, the development of a fast and efficient compression algorithm specific to genome data is an important and pressing issue. RESULTS: We have developed a referential lossless genome data compression algorithm with better performance than previous algorithms. According to a carefully designed matching strategy selection mechanism, the advantages of local matching and global matching are reasonably combined together to improve the description efficiency of the matched sub-strings. The effects of the length and the position of matched sub-strings to the compression efficiency are jointly taken into consideration. The proposed algorithm can compress the FASTA data of complete human genomes, each of which is about 3 GB, in about 18 min. The compressed file sizes are ranging from a few megabytes to about forty megabytes. The averaged compression ratio is higher than that of the state-of-the-art genome compression algorithms, the time complexity is at the same order of the best-known algorithms. AVAILABILITY AND IMPLEMENTATION: https://github.com/jhchen5/SCCG. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Learnt clauses in CDCL SAT solvers often contain redundant literals. This may have a negative impact on performance because redundant literals may deteriorate both the effectiveness of Boolean constraint propagation and the quality of subsequent learnt clauses. To overcome this drawback, we define a new inprocessing SAT approach which eliminates redundant literals from learnt clauses by applying Boolean constraint propagation. Learnt clause minimization is activated before the SAT solver triggers some selected restarts, and affects only some learnt clauses during the search process. Moreover, we conducted an empirical evaluation on instances coming from the hard combinatorial and application categories of recent SAT competitions. The results show that a remarkable number of additional instances are solved when the approach is incorporated into five of the best performing CDCL SAT solvers (Glucose, TC_Glucose, COMiniSatPS, MapleCOMSPS and MapleCOMSPS_LRB).