As the use of SAT solvers as core engines in EDA applications grows, it becomes increasingly important to validate their correctness. In this paper, we describe the implementation of an independent resolution-based checking procedure that can check the validity of unsatisfiable claims produced by the SAT solver zchaff. We examine the practical implementation issues of such a checker and describe two implementations with different pros and cons. Experimental results show low overhead for the checking process. Our checker can work with many other modern SAT solvers with minor modifications, and it can provide information for debugging when checking fails. Finally we describe additional results that can be obtained by the validation process and briefly discuss their applications. 1. Introduction and Previous

Abstract. Much attention has been given in recent years to the problem of

This paper describes a new algorithm for extracting unsatisfiable subformulas from a given unsatisfiable CNF formula. Such unsatisfiable “cores ” can be very helpful in diagnosing the causes of infeasibility in large systems. Our algorithm is unique in that it adapts the “learning process ” of a modern SAT solver to identify unsatisfiable subformulas rather than search for satisfying assignments. Compared to existing approaches, this method can be viewed as a bottom-up core extraction procedure which can be very competitive when the core sizes are much smaller than the original formula size. Repeated runs of the algorithm with different branching orders yield different cores. We present experimental results on a suite of large automotive benchmarks showing the performance of the algorithm and highlighting its ability to locate not just one but several cores.

Certifying the correctness of a SAT solver is straightforward for satisfiable instances of SAT.

Abstract. Much research in the area of constraint processing has recently been focused on extracting small unsatisfiable “cores ” from unsatisfiable constraint systems with the goal of finding minimal unsatisfiable subsets (MUSes). While most techniques have provided ways to find an approximation of an MUS (not necessarily minimal), we have developed a sound and complete algorithm for producing all MUSes of an unsatisfiable constraint system. In this paper, we describe a useful relationship between satisfiable and unsatisfiable subsets of constraints that we subsequently use as the foundation for MUS extraction algorithms, implemented for Boolean satisfiability constraints. The algorithms provide a framework with which many related subproblems can be solved, including relaxations of completeness to handle intractable instances, and we develop several variations of the basic algorithms to illustrate this. Experimental results demonstrate the performance of our algorithms, showing how the base algorithms run quickly on many instances, while the variations are valuable for producing results on instances whose complete results are intractably large. Furthermore, our algorithms are shown to perform better than the existing algorithms for solving either of the two distinct phases of our approach. 1.

After establishing the unsatisfiability of a SAT instance encoding a typical design task, there is a practical need to identify its minimal unsatisfiable subsets, which pinpoint the reasons for the infeasibility of the design. Due to the potentially expensive computation, existing tools for the extraction of unsatisfiable subformulas do not guarantee the minimality of the results. This paper describes a practical algorithm that decides the minimal unsatisfiability of any CNF formula through BDD manipulation. This algorithm has a worse-case complexity that is exponential only in the treewidth of the CNF formula. We provide an empirical evaluation of the algorithm, highlighting its efficiency on a set of hard problems as well as its ability to work with existing subformula extraction tools to achieve optimal results.

The task of extracting an unsatisfiable core for a given Boolean formula has been finding more and more applications in recent years. The only existing approach that scales well for large real-world formulas exploits the ability of modern SAT solvers to produce resolution refutations. However, the resulting unsatisfiable cores are suboptimal. We propose a new algorithm for minimal unsatisfiable core extraction, based on a deeper exploration of resolution-refutation properties. Experimental results, confirming that the algorithm is able to find minimal unsatisfiable cores for well-known formal verification benchmarks, are provided.

Recent advances in propositional satisfiability (SAT) include studying the hidden structure of unsatisfiable formulas, i.e. explaining why a given formula is unsatisfiable. Although theoretical work on the topic has been developed in the past, only recently two empirical successful approaches have been proposed: extracting unsatisfiable cores and identifying strong backdoors. An unsatisfiable core is a subset of clauses that defines a sub-formula that is also unsatisfiable, whereas a strong backdoor defines a subset of variables which assigned with all values allow concluding that the formula is unsatisfiable. The contribution of this paper is two-fold. First, we study the relation between the search complexity of unsatisfiable random 3-SAT formulas and the sizes of unsatisfiable cores and strong backdoors. For this purpose, we use an existing algorithm which uses an approximated approach for calculating these values. Second, we introduce a new algorithm that optimally reduces the size of unsatisfiable cores and strong backdoors, thus giving more accurate results. Experimental results indicate that the search complexity of unsatisfiable random 3-SAT formulas is related with the size of unsatisfiable cores and strong backdoors. 1.

Abstract. We tackle the problem of finding a smallest-cardinality MUS (SMUS) of a given formula. The SMUS provides a succinct explanation of infeasibility and is valuable for applications that rely on such explanations. We present a branch-and-bound algorithm that utilizes iterative MAXSAT solutions to generate lower and upper bounds on the size of the SMUS, and branch on specific subformulas to find it. We report experimental results on formulas from DIMACS and DaimlerChrysler product configuration suites. 1

Explaining the causes of infeasibility of Boolean formulas has practical applications in various fields. We are generally interested in a minimum explanation of infeasibility that excludes irrelevant information. A