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Results for “boolean satisfiability (SAT)”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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4 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Posiform planting: generating QUBO instances for benchmarking

We are interested in benchmarking both quantum annealing and classical algorithms for minimizing quadratic unconstrained binary optimization (QUBO) problems. Such problems are NP-hard in general, implying that the exact minima of randomly generated instances are hard to find and thus typically unknown. While brute forcing smaller instances is possible, such instances are typically not interesting due to being too easy for both quantum and classical algorithms. In this contribution, we propose a novel method, called posiform planting , for generating random QUBO instances of arbitrary size with known optimal solutions, and use those instances to benchmark the sampling quality of four D-Wave quantum annealers utilizing different interconnection structures (Chimera, Pegasus, and Zephyr hardware graphs) and the simulated annealing algorithm. Posiform planting differs from many existing methods in two key ways. It ensures the uniqueness of the planted optimal solution, thus avoiding groundstate degeneracy, and it enables the generation of QUBOs that are tailored to a given hardware connectivity structure, provided that the connectivity is not too sparse. Posiform planted QUBOs are a type of 2-SAT boolean satisfiability combinatorial optimization problems. Our experiments demonstrate the capability of the D-Wave quantum annealers to sample the optimal planted solution of combinatorial optimization problems with up to 5, 627 qubits.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Applying Incremental, Inductive Model Checking to the Modal Mu Calculus

This work aims to demonstrate that an incremental inductive model checking algorithm built on top of Boolean satisfiability (SAT) solvers can be extended to support modal mu calculus (MMC) formulas. The resulting algorithm, called modal mu calculus model checking using myopic constraints (MC3), solves MMC model checking problems over Boolean labeled transition systems (LTSs). MMC subsumes simple invariance/reachability (as solved by the IC3 algorithm), linear temporal logic (LTL, as solved by the fair algorithm), computation tree logic (CTL, as solved by IICTL), and CTL* (which in turn subsumes LTL and CTL, but was not previously supported by any incremental inductive algorithm). The algorithm is implemented in a prototype solver, mc3.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Reconfigurable Very Long Instruction Word (VLIW) Processor

Future NASA missions will depend on radiation-hardened, power-efficient processing systems-on-a-chip (SOCs) that consist of a range of processor cores custom tailored for space applications. Aries Design Automation, LLC, has developed a processing SOC that is optimized for software-defined radio (SDR) uses. The innovation implements the Institute of Electrical and Electronics Engineers (IEEE) RazorII voltage management technique, a microarchitectural mechanism that allows processor cores to self-monitor, self-analyze, and selfheal after timing errors, regardless of their cause (e.g., radiation; chip aging; variations in the voltage, frequency, temperature, or manufacturing process). This highly automated SOC can also execute legacy PowerPC 750 binary code instruction set architecture (ISA), which is used in the flight-control computers of many previous NASA space missions. In developing this innovation, Aries Design Automation has made significant contributions to the fields of formal verification of complex pipelined microprocessors and Boolean satisfiability (SAT) and has developed highly efficient electronic design automation tools that hold promise for future developments.

Velev, Miroslav N.↗
NASA NTRS2005

Biased Randomized Algorithm for Fast Model-Based Diagnosis

A biased randomized algorithm has been developed to enable the rapid computational solution of a propositional- satisfiability (SAT) problem equivalent to a diagnosis problem. The closest competing methods of automated diagnosis are described in the preceding article "Fast Algorithms for Model-Based Diagnosis" and "Two Methods of Efficient Solution of the Hitting-Set Problem" (NPO-30584), which appears elsewhere in this issue. It is necessary to recapitulate some of the information from the cited articles as a prerequisite to a description of the present method. As used here, "diagnosis" signifies, more precisely, a type of model-based diagnosis in which one explores any logical inconsistencies between the observed and expected behaviors of an engineering system. The function of each component and the interconnections among all the components of the engineering system are represented as a logical system. Hence, the expected behavior of the engineering system is represented as a set of logical consequences. Faulty components lead to inconsistency between the observed and expected behaviors of the system, represented by logical inconsistencies. Diagnosis - the task of finding the faulty components - reduces to finding the components, the abnormalities of which could explain all the logical inconsistencies. One seeks a minimal set of faulty components (denoted a minimal diagnosis), because the trivial solution, in which all components are deemed to be faulty, always explains all inconsistencies. In the methods of the cited articles, the minimal-diagnosis problem is treated as equivalent to a minimal-hitting-set problem, which is translated from a combinatorial to a computational problem by mapping it onto the Boolean-satisfiability and integer-programming problems. The integer-programming approach taken in one of the prior methods is complete (in the sense that it is guaranteed to find a solution if one exists) and slow and yields a lower bound on the size of the minimal diagnosis. In contrast, the present approach is incomplete and fast and yields an upper bound on the size of the minimal diagnosis.

Williams, Colin↗