Boolean Satisfiability Research Articles

Non-Hermitian ground-state-searching algorithm enhanced by a variational toolbox

Ground-state preparation for a given Hamiltonian is a common quantum-computing task of great importance and has relevant applications in quantum chemistry, computational material modeling, and combinatorial optimization. We consider an approach to simulate dissipative non-Hermitian Hamiltonian quantum dynamics using Hamiltonian simulation techniques to efficiently recover the ground state of a target Hamiltonian. The proposed method facilitates the energy transfer by repeatedly projecting ancilla qubits to the desired state, rendering the effective non-Hermitian Hamiltonian evolution on the system qubits. To make the method more resource friendly in the noisy intermediate-scale quantum (NISQ) and early fault-tolerant era, we combine the non-Hermitian projection algorithm with multiple variational gadgets, including variational module enhancement and variational state recording, to reduce the required circuit depth and avoid the exponentially vanishing success probability for postselections. We compare our method, the non-Hermitian-variational algorithm, with a pure variational method, the quantum approximate optimization algorithm (QAOA), for solving the 3-SAT problem and preparing the ground state for the transverse field Ising model. As demonstrated by numerical evidence, the non-Hermitian-variational algorithm outperforms QAOA in convergence speed with improved quantum resource efficiency.

Design strategies for the self-assembly of polyhedral shells.

The control over the self-assembly of complex structures is a long-standing challenge of material science, especially at the colloidal scale, as the desired assembly pathway is often kinetically derailed by the formation of amorphous aggregates. Here, we investigate in detail the problem of the self-assembly of the three Archimedean shells with five contact points per vertex, i.e., the icosahedron, the snub cube, and the snub dodecahedron. We use patchy particles with five interaction sites (or patches) as model for the building blocks and recast the assembly problem as a Boolean satisfiability problem (SAT) for the patch-patch interactions. This allows us to find effective designs for all targets and to selectively suppress unwanted structures. By tuning the geometrical arrangement and the specific interactions of the patches, we demonstrate that lowering the symmetry of the building blocks reduces the number of competing structures, which in turn can considerably increase the yield of the target structure. These results cement SAT-assembly as an invaluable tool to solve inverse design problems.

Redundancy Attack: Breaking Logic Locking Through Oracleless Rationality Analysis

During the last decade, logic locking has been proposed to protect integrated circuits against piracy and reverse engineering threats. Functional pruning attacks, such as the Boolean satisfiability (SAT) attack, have greatly challenged the security of logic locking techniques, yet they require access to an oracle and suffer from muted efficacy on inherently SAT-hard circuits. In this article, we present a novel oracleless attack on both XOR and MUX-based logic locking by analyzing the redundancy level deviation under different keys. Our fundamental insight is that incorrect keys would produce circuits that violate universally followed design principles, prominent among which stands the minimization of logic redundancy. We leverage the redundancy-level deviation produced by individual and pairs of key bits to recover key bit values and establish pairwise equivalence, leading to an efficient nearly linear time attack algorithm. We experimentally verify that the proposed attack quickly unveils more than half of the key bits with high accuracy on 21 ISCAS’85 and MCNC circuits locked with a variety of locking techniques. To fortify logic locking in the face of this successful redundancy attack, we supplement this article with a key gate insertion methodology that smooths the deviation in redundancy level. We achieve this goal by selecting key gates that exhibit pairwise dissociation among a set of individually secure locations, which effectively breaks down correlations in arbitrary sets of key gates and, thus, imposes a consistent redundancy level throughout the key space. Experimental results confirm the strong redundancy attack resilience of the proposed defense strategy.

Automated streamliner portfolios for constraint satisfaction problems

Constraint Programming (CP) is a powerful technique for solving large-scale combinatorial problems. Solving a problem proceeds in two distinct phases: modelling and solving. Effective modelling has a huge impact on the performance of the solving process. Even with the advance of modern automated modelling tools, search spaces involved can be so vast that problems can still be difficult to solve. To further constrain the model, a more aggressive step that can be taken is the addition of streamliner constraints, which are not guaranteed to be sound but are designed to focus effort on a highly restricted but promising portion of the search space. Previously, producing effective streamlined models was a manual, difficult and time-consuming task. This paper presents a completely automated process to the generation, search and selection of streamliner portfolios to produce a substantial reduction in search effort across a diverse range of problems. The results demonstrate a marked improvement in performance for both Chuffed, a CP solver with clause learning, and lingeling, a modern SAT solver.

Solving boolean satisfiability problems with the quantum approximate optimization algorithm

Solving boolean satisfiability problems with the quantum approximate optimization algorithm

Hybrid divide-and-conquer approach for tree search algorithms

One of the challenges of quantum computers in the near- and mid- term is the limited number of qubits we can use for computations. Finding methods that achieve useful quantum improvements under size limitations is thus a key question in the field. In this vein, it was recently shown that a hybrid classical-quantum method can help provide polynomial speed-ups to classical divide-and-conquer algorithms, even when only given access to a quantum computer much smaller than the problem itself. In this work, we study the hybrid divide-and-conquer method in the context of tree search algorithms, and extend it by including quantum backtracking, which allows better results than previous Grover-based methods. Further, we provide general criteria for polynomial speed-ups in the tree search context, and provide a number of examples where polynomial speed ups, using arbitrarily smaller quantum computers, can be obtained. We provide conditions for speedups for the well known algorithm of DPLL, and we prove threshold-free speed-ups for the PPSZ algorithm (the core of the fastest exact Boolean satisfiability solver) for well-behaved classes of formulas. We also provide a simple example where speed-ups can be obtained in an algorithm-independent fashion, under certain well-studied complexity-theoretical assumptions. Finally, we briefly discuss the fundamental limitations of hybrid methods in providing speed-ups for larger problems.

Extended Ising Machine with Additional Non-quadratic Cost Functions

We have developed a method to extend the functionality of the Ising machine, which currently has an energy function limited to a binary-quadratic form. The proposed method utilizes auxiliary variables dependent on the decision variables of the problem to be solved so that it can be accelerated by hardware parallel processing. Auxiliary variables add third-order or higher terms or rectified linear unit-type nonlinear functions to the binary-quadratic energy function of the Ising machine, modifying the value of the energy difference resulting from the reversal of the decision variable. To enable parallel computation, the computation of the energy change uses information that can be accessed locally by the decision neurons. We confirmed that the proposed method works for the 0/1 linear knapsack problem, the quadratic knapsack problem, the 3-SAT problem, and the random 3-XORST problem.

SAT-aided Automatic Search of Boomerang Distinguishers for ARX Ciphers

In Addition-Rotation-Xor (ARX) ciphers, the large domain size obstructs the application of the boomerang connectivity table. In this paper, we explore the problem of computing this table for a modular addition and the automatic search of boomerang characteristics for ARX ciphers. We provide dynamic programming algorithms to efficiently compute this table and its variants. These algorithms are the most efficient up to now. For the boomerang connectivity table, the execution time is 42(n − 1) simple operations while the previous algorithm costs 82(n − 1) simple operations, which generates a smaller model in the searching phase. After rewriting these algorithms with boolean expressions, we construct the corresponding Boolean Satisfiability Problem models. Two automatic search frameworks are also proposed based on these models. This is the first time bringing the SAT-aided automatic search techniques into finding boomerang attacks on ARX ciphers. Finally, under these frameworks, we find out the first verifiable 10-round boomerang trail for SPECK32/64 with probability 2−29.15 and a 12-round trail for SPECK48/72 with probability 2−44.15. These are the best distinguishers for them so far. We also perceive that the previous boomerang attacks on LEA are constructed with an incorrect computation of the boomerang connection probability. The result is then fixed by our frameworks.

Decision tree post-pruning without loss of accuracy using the SAT-PP algorithm with an empirical evaluation on clinical data

A decision tree (DT) is one of the most popular and efficient techniques in data mining. Specifically, in the clinical domain, DTs have been widely used thanks to their relatively easy explainable nature, efficient computation time, and relatively accurate predictions. However, some DT constriction algorithms may produce a large tree size structure which is difficult to understand and often leads to misclassification of data in the testing process due to poor generalization. Post pruning (PP) algorithms have been introduced to reduce the size of the tree structure with a minor (or not at all) decrease in the accuracy of classification while trying to improve the model’s generalization. In this paper, we propose a new Boolean satisfiability (SAT) based PP algorithm called SAT-PP. Our algorithm reduces the tree size while preserving the accuracy of the unpruned tree. We implemented our algorithm on a medical-related classification data sets since in medical-related tasks we emphatically try to avoid decreasing the model’s performance when better training is not an option. Namely, in the case of medical-related tasks, one may prefer an unpruned DT model to a pruned DT model with worse performance. Indeed, we empirically obtained that the SAT-PP algorithm produce the same accuracy and F1 score as the DT model without PP while statistically significantly reducing the model size and as a result computation time (6.8%). In addition, we compared the proposed algorithm with other PP algorithms and found similar generalization capabilities.

Quantum hybrid algorithm for solving SAT problem

Combinatorial problems usually have a large search space, and almost all classical algorithms for solving this class of problems are inefficient for real-life input sizes. Quantum algorithms have been introduced with the aim of reducing computational time. In addition, to speed up the computation while solving combinatorial problems, another approach is to reduce the search space and focus on a restricted area. In this work, we study a parameter that helps reduce the search space for the solution to the satisfiability problem (SAT). We propose an approach based on Grover’s algorithm that considers the defined parameter to reduce the search space for the solution of the SAT problem. Our algorithm has a complexity of the order O(N/S), or O(N/lS) if there are l good solutions among the initial solutions, with N the number of potential solutions and S the reduction or subdivision factor of the space.

Verified Propagation Redundancy and Compositional UNSAT Checking in CakeML

Modern SAT solvers can emit independently-checkable proof certificates to validate their results. The state-of-the-art proof system that allows for compact proof certificates is propagation redundancy (textsf{PR}). However, the only existing method to validate proofs in this system with a formally verified tool requires a transformation to a weaker proof system, which can result in a significant blowup in the size of the proof and increased proof validation time. This article describes the first approach to formally verify textsf{PR} proofs on a succinct representation. We present (i) a new Linear PR (LPR) proof format, (ii) an extension of the DPR-trim tool to efficiently convert textsf{PR} proofs into LPR format, and (iii) cake_lpr, a verified LPR proof checker developed in CakeML. We also enhance these tools with (iv) a new compositional proof format designed to enable separate (parallel) proof checking. The LPR format is backwards compatible with the existing LRAT format, but extends LRAT with support for the addition of textsf{PR} clauses. Moreover, cake_lpr is verified using CakeML ’s binary code extraction toolchain, which yields correctness guarantees for its machine code (binary) implementation. This further distinguishes our clausal proof checker from existing checkers because unverified extraction and compilation tools are removed from its trusted computing base. We experimentally show that: LPR provides efficiency gains over existing proof formats; cake_lpr ’s strong correctness guarantees are obtained without significant sacrifice in its performance; and the compositional proof format enables scalable parallel proof checking for large proofs.

Computing generating sets of minimal size in finite algebras

We present an algorithm for calculating a minimal generating set of a finite algebra. Despite the fact that the problem is in NP, a single call to a SAT solver is impractical since the encoding is cubic. Instead, the proposed algorithm solves a series of smaller subproblems. The individual subproblems are formulated as integer linear programs (ILP) that are solved by an off-the-shelf solver. Our implementation shows that the proposed algorithm is highly efficient and is able to compute minimal generators for algebras of orders approximately 2000.In our experiments we focus on Moufang loops, a variety of loops with properties close to groups. For Moufang loops of prime power order, we are able to calculate a minimal generating set by another method, using theoretical results on the Frattini subloop and algorithms for permutation groups, of which some are reported here for the first time. This second method does not cover all cases, but in the covered cases it serves as a check of correctness of the ILP-based algorithm.

Using a SAT Solver to Find Interesting Sets of Nonstandard Dice

We describe a family of Boolean satisfiability (SAT) problems for which each solution corresponds to a unique set of nonstandard dice. We show that we can control the relationships between the dice in each solution by imposing a set of cardinality constraints on the variables in the corresponding SAT problem. We then present examples of interesting sets of nonstandard dice that we found by solving such problems. In particular, we describe a set of 19 five-sided dice that realize the Paley tournament on 19 vertices. Furthermore, we show that this set of dice is minimal in the sense that no set of 19 dice with less than five sides can realize the Paley tournament on 19 vertices.

Advanced tools and methods for treewidth-based problem solving

Abstract Computer programs, so-called solvers, for solving the well-known Boolean satisfiability problem (Sat) have been improving for decades. Among the reasons, why these solvers are so fast, is the implicit usage of the formula’s structural properties during solving. One of such structural indicators is the so-called treewidth, which tries to measure how close a formula instance is to being easy (tree-like). This work focuses on logic-based problems and treewidth-based methods and tools for solving them. Many of these problems are also relevant for knowledge representation and reasoning (KR) as well as artificial intelligence (AI) in general. We present a new type of problem reduction, which is referred to by decomposition-guided (DG). This reduction type forms the basis to solve a problem for quantified Boolean formulas (QBFs) of bounded treewidth that has been open since 2004. The solution of this problem then gives rise to a new methodology for proving precise lower bounds for a range of further formalisms in logic, KR, and AI. Despite the established lower bounds, we implement an algorithm for solving extensions of Sat efficiently, by directly using treewidth. Our implementation is based on finding abstractions of instances, which are then incrementally refined in the process. Thereby, our observations confirm that treewidth is an important measure that should be considered in the design of modern solvers.

Balanced covering arrays: A classification of covering arrays and packing arrays via exact methods

AbstractIn this paper we investigate the intersections of classes of covering arrays (CAs) and packing arrays (PAs). The arrays appearing in these intersections obey to upper and lower bounds regarding the appearance of tuples in sub‐matrices—we call these arrays balanced covering arrays. We formulate and formalize first observations for which upper and lower bounds on the appearance of tuples it is of interest to consider these intersections of CAs and PAs. Outside of these bounds the intersections will be either empty, for the case of too restrictive constraints, or equal to the maximum element in the emerging lattices, for the case of too weak constraints. We present a column extension algorithm for classification of nonequivalent balanced CAs that uses a SAT solver or a pseudo‐Boolean (PB) solver to compute the columns suitable for array extension together with a lex‐leader ordering to identify unique representatives for each equivalence class of balanced CAs. These computations bring to light a dissection of classes of CAs that is partially nested due to the nature of the considered intersections. These dissections can be trivial, containing only a single type of balanced CAs, or can also appear as highly structured containing multiple nested types of balanced CAs. Our results indicate that balanced CAs are an interesting class of designs that is rich of structure.

Top-k Learned Clauses for Modern SAT Solvers

Clause Learning is one of the most important components of a conflict driven clause learning (CDCL) SAT solver that is effective on industrial SAT instances. Since the number of learned clauses is proved to be exponential in the worst case, it is necessary to identify the most relevant clauses to maintain and delete the irrelevant ones. As reported in the literature, several learned clauses deletion strategies have been proposed. However the diversity in both the number of clauses to be removed at each step of reduction and the results obtained with each strategy increase the difficulty to determine which criterion is better. Thus, the problem to select which learned clauses are to be removed during the search step remains very challenging. In this paper, we propose a novel approach to identify the most relevant learned clauses without favoring or excluding any of the proposed measures, but by adopting the notion of dominance relationship among those measures. Our approach bypasses the problem of difference in the results obtained with different measures and reaches a compromise between the measures assessments. Furthermore, the proposed approach also reduces the non-trivial problem of choosing the number of clauses to delete at each reduction of the learned clause database.

Generating Random SAT Instances: Multiple Solutions could be Predefined and Deeply Hidden

The generation of SAT instances is an important issue in computer science, and it is useful for researchers to verify the effectiveness of SAT solvers. Addressing this issue could inspire researchers to propose new search strategies. SAT problems exist in various real-world applications, some of which have more than one solution. However, although several algorithms for generating random SAT instances have been proposed, few can be used to generate hard instances that have multiple predefined solutions. In this paper, we propose the KHidden-M algorithm to generate SAT instances with multiple predefined solutions that could be hard to solve by the local search strategy when the number of predefined solutions is small enough and the Hamming distance between them is not less than half of the solution length. Specifically, first, we generate an SAT instance that is satisfied by all of the predefined solutions. Next, if the generated SAT instance does not satisfy the hardness condition, then a strategy will be conducted to adjust clauses through multiple iterations to improve the hardness of the whole instance. We propose three strategies to generate the SAT instance in the first part. The first strategy is called the random strategy, which randomly generates clauses that are satisfied by all of the predefined solutions. The other two strategies are called the estimating strategy and greedy strategy, and using them, we attempt to generate an instance that directly satisfies or is closer to the hardness condition for the local search strategy. We employ two SAT solvers (i.e., WalkSAT and Kissat) to investigate the hardness of the SAT instances generated by our algorithm in the experiments. The experimental results show the effectiveness of the random, estimating and greedy strategies. Compared to the state-of-the-art algorithm for generating SAT instances with predefined solutions, namely, M-hidden, our algorithm could be more effective in generating hard SAT instances.

Extension-based semantics for incomplete argumentation frameworks: properties, complexity and algorithms

AbstractIncomplete Argumentation Frameworks (IAFs) have been defined to incorporate some qualitative uncertainty in abstract argumentation: information such as ‘I am not sure whether this argument exists’ or ‘I am not sure whether this argument attacks that one’ can be expressed. Reasoning with IAFs is classically based on a set of completions, i.e. standard argumentation frameworks (AFs) that represent the possible worlds encoded in the IAF. The number of these completions may be exponential with respect to the number of arguments in the IAF. This leads, in some cases, to an increase of the complexity of reasoning, compared to the complexity of standard AFs. In this paper, we follow an approach that was initiated for Partial Argumentation Frameworks (PAFs) (a subclass of IAFs), which consists in defining new forms of conflict-freeness and defense, the properties that underly the definition of Dung’s semantics for AFs. We generalize these semantics from PAFs to IAFs. We show that, among three possible types of admissibility, only two of them satisfy some desirable properties. We use them to define two new families of extension-based semantics. We study the properties of these semantics, and in particular, we show that their complexity remains the same as in the case of Dung’s AFs. Finally, we propose a logical encoding of these semantics, and we show experimentally that this encoding can be used efficiently to reason with IAFs, thanks to the power of modern SAT solvers.

Evaluating state-of-the-art # SAT solvers on industrial configuration spaces

Product lines are widely used to manage families of products that share a common base of features. Typically, not every combination (configuration) of features is valid. Feature models are a de facto standard to specify valid configurations and allow standardized analyses on the variability of the underlying system. A large variety of such analyses depends on computing the number of valid configurations. To analyze feature models, they are typically translated to propositional logic. This allows to employ # SAT solvers that compute the number of satisfying assignments of the propositional formula translated from a feature model. However, the # SAT problem is generally assumed to be even harder than SAT and its scalability when applied to feature models has only been explored sparsely. Our main contribution is an investigation of the performance of off-the-shelf # SAT solvers on computing the number of valid configurations for industrial feature models. We empirically evaluate 21 publicly available # SAT solvers on 130 feature models from 15 subject systems. Our results indicate that current solvers master a majority of the evaluated systems (13/15) with the fastest solvers requiring less than one second for each successfully evaluated feature model. However, there are two complex systems for which none of the evaluated solvers scales. For the given experiment design, the solvers that consumed the least runtime are sharpSAT (2.5 seconds in sum for the 13 systems) and Ganak (3.5 seconds).

Improving AMulet2 for verifying multiplier circuits using SAT solving and computer algebra

Verifying arithmetic circuits and most prominently multiplier circuits is an important problem which in practice is still considered to be challenging. One of the currently most successful verification techniques relies on algebraic reasoning. In this article, we present AMulet2, a fully automatic tool for verification of integer multipliers combining SAT solving and computer algebra. Our tool models multipliers given as and-inverter graphs as a set of polynomials and applies preprocessing techniques based on elimination theory of Gröbner bases. Finally, it uses a polynomial reduction algorithm to verify the correctness of the given circuit. AMulet2 is a re-factorization and improved re-implementation of our previous verification tool AMulet1 and cannot only be used as a stand-alone tool but also serves as a polynomial reasoning framework. We present a novel XOR-based slicing approach and discuss improvements on the data structures including monomial sharing.