Lynx: A Programmatic SAT Solver for the RNA-folding Problem

15th International Conference, Trento, Italy, June 17-20, 2012. ProceedingsThis paper introduces Lynx, an incremental programmatic SAT solver that allows non-expert users to introduce domain-specific code into modern conflict-driven clause-learning (CDCL) SAT solvers, thus enabling users to guide the behavior of the solver. The key idea of Lynx is a callback interface that enables non-expert users to specialize the SAT solver to a class of Boolean instances. The user writes specialized code for a class of Boolean formulas, which is periodically called by Lynx’s search routine in its inner loop through the callback interface. The user-provided code is allowed to examine partial solutions generated by the solver during its search, and to respond by adding CNF clauses back to the solver dynamically and incrementally. Thus, the user-provided code can specialize and influence the solver’s search in a highly targeted fashion. While the power of incremental SAT solvers has been amply demonstrated in the SAT literature and in the context of DPLL(T), it has not been previously made available as a programmatic API that is easy to use for non-expert users. Lynx’s callback interface is a simple yet very effective strategy that addresses this need. We demonstrate the benefits of Lynx through a case-study from computational biology, namely, the RNA secondary structure prediction problem. The constraints that make up this problem fall into two categories: structural constraints, which describe properties of the biological structure of the solution, and energetic constraints, which encode quantitative requirements that the solution must satisfy. We show that by introducing structural constraints on-demand through user provided code we can achieve, in comparison with standard SAT approaches, upto 30x reduction in memory usage and upto 100x reduction in time

Integrating Conflict Driven Clause Learning to Local Search

This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach for propositional satisfiability. It combines local search and conflict driven clause learning (CDCL) scheme. Each time the local search part reaches a local minimum, the CDCL is launched. For SAT problems it behaves like a tabu list, whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable sub-formula (MUS). Experimental results show good performances on many classes of SAT instances from the last SAT competitions

Solving hard industrial combinatorial problems with SAT

The topic of this thesis is the development of SAT-based techniques and tools for solving industrial combinatorial problems. First, it describes the architecture of state-of-the-art SAT and SMT Solvers based on the classical DPLL procedure. These systems can be used as black boxes for solving combinatorial problems. However, sometimes we can increase their efficiency with slight modifications of the basic algorithm. Therefore, the study and development of techniques for adjusting SAT Solvers to specific combinatorial problems is the first goal of this thesis. Namely, SAT Solvers can only deal with propositional logic. For solving general combinatorial problems, two different approaches are possible: - Reducing the complex constraints into propositional clauses. - Enriching the SAT Solver language. The first approach corresponds to encoding the constraint into SAT. The second one corresponds to using propagators, the basis for SMT Solvers. Regarding the first approach, in this document we improve the encoding of two of the most important combinatorial constraints: cardinality constraints and pseudo-Boolean constraints. After that, we present a new mixed approach, called lazy decomposition, which combines the advantages of encodings and propagators. The other part of the thesis uses these theoretical improvements in industrial combinatorial problems. We give a method for efficiently scheduling some professional sport leagues with SAT. The results are promising and show that a SAT approach is valid for these problems. However, the chaotical behavior of CDCL-based SAT Solvers due to VSIDS heuristics makes it difficult to obtain a similar solution for two similar problems. This may be inconvenient in real-world problems, since a user expects similar solutions when it makes slight modifications to the problem specification. In order to overcome this limitation, we have studied and solved the close solution problem, i.e., the problem of quickly finding a close solution when a similar problem is considered

Towards a Theoretical Understanding of the Power of Restart in SAT solvers

Restart policy is a widely used class of techniques integral to the efficiency of conflict-driven clause-learning (CDCL) SAT solvers. While the utility of such policies has been well-established, to-date we still lack a deep theoretical understanding of why restart policies are crucial to the power of CDCL SAT solvers. In this paper, we provide a series of results that theoretically establish the power of restarts for various models of Boolean SAT solvers. More precisely, we make the following contributions. First, we show that certain model of CDCL solvers with restarts are no more powerful from a proof-complexity theoretic point of view than the same configurations without restarts. Second, we define \textit{decision depth} for DPLL proofs of an unsatisfiable formula $\varphi$, and then we relate decision depth of $\varphi$ and size of DPLL proofs (or the running time of a DPLL based solver) for $\varphi$. Third, we introduce a new class of satisfiable instances called $Ladder_n$, then we use decision depth as a tool to proved that a drunk style DPLL solver with restarts can solve $Ladder_n$ in polynomial time with high probability while the solvers with the same configuration without restarts have exponential run time with high probability. Finally, the crucial insight that drives this line of research is the fact that restarts add proof-theoretic or algorithmic power to solver configurations by compensating for the weaknesses of some other important heuristics like branching or value selection or clause learning

SAT-based preimage attacks on SHA-1

Hash functions are important cryptographic primitives which map arbitrarily long messages to fixed-length message digests in such a way that: (1) it is easy to compute the message digest given a message, while (2) inverting the hashing process (e.g. finding a message that maps to a specific message digest) is hard. One attack against a hash function is an algorithm that nevertheless manages to invert the hashing process. Hash functions are used in e.g. authentication, digital signatures, and key exchange. A popular hash function used in many practical application scenarios is the Secure Hash Algorithm (SHA-1). In this thesis we investigate the current state of the art in carrying out preimage attacks against SHA-1 using SAT solvers, and we attempt to find out if there is any room for improvement in either the encoding or the solving processes. We run a series of experiments using SAT solvers on encodings of reduced-difficulty versions of SHA-1. Each experiment tests one aspect of the encoding or solving process, such as e.g. determining whether there exists an optimal restart interval or determining which branching heuristic leads to the best average solving time. An important part of our work is to use statistically sound methods, i.e. hypothesis tests which take sample size and variation into account. Our most important result is a new encoding of 32-bit modular addition which significantly reduces the time it takes the SAT solver to find a solution compared to previously known encodings. Other results include the fact that reducing the absolute size of the search space by fixing bits of the message up to a certain point actually results in an instance that is harder for the SAT solver to solve. We have also identified some slight improvements to the parameters used by the heuristics of the solver MiniSat; for example, contrary to assertions made in the literature, we find that using longer restart intervals improves the running time of the solver

Approaches to grid-based SAT solving

In this work we develop techniques for using distributed computing resources to efficiently solve instances of the propositional satisfiability problem (SAT). The computing resources considered in this work are assumed to be geographically distributed and connected by a non-dedicated network. Such systems are typically referred to as computational grid environments. The time a modern SAT solver consumes while solving an instance varies according to a random distribution. Unlike many other methods for distributed SAT solving, this work identifies the random distribution as a valuable resource for solving-time reduction. The methods which use randomness in the run times of a search algorithm, such as the ones discussed in this work, are examples of multi-search. The main contribution of this work is in developing and analyzing the multi-search approach in SAT solving and showing its efficiency with several experiments. For the purpose of the analysis, the work introduces a grid simulation model which captures several of the properties of a grid environment which are not observed in more traditional parallel computing systems. The work develops two algorithmic frameworks for multi-search in SAT. The first, SDSAT, is based on using properties of the distribution of the solving time so that the expected time required to solve an instance is reduced. Based on the analysis of SDSAT, the work proposes an algorithm for efficiently using large number of computing resources simultaneously to solve collections of SAT instances. The analysis of SDSAT also motivates the second algorithmic framework, CL-SDSAT. The framework is used to efficiently solve many industrial SAT instances by carefully combining information learned in the distributed SAT solvers. All methods described in the work are directly applicable in a wide range of grid environments and can be used together with virtually unmodified state-of-the-art SAT solvers. The methods are experimentally verified using standard benchmark SAT instances in a production-level grid environment. The experiments show that using the relatively simple methods developed in the work, SAT instances which cannot be solved efficiently in sequential settings can be now solved in a grid environment

Learning Heuristics for Quantified Boolean Formulas through Deep Reinforcement Learning

We demonstrate how to learn efficient heuristics for automated reasoning algorithms for quantified Boolean formulas through deep reinforcement learning. We focus on a backtracking search algorithm, which can already solve formulas of impressive size - up to hundreds of thousands of variables. The main challenge is to find a representation of these formulas that lends itself to making predictions in a scalable way. For a family of challenging problems, we learned a heuristic that solves significantly more formulas compared to the existing handwritten heuristics