ALIAS: A Modular Tool for Finding Backdoors for SAT

Kochemazov, Stepan; Zaikin, Oleg

doi:10.1007/978-3-319-94144-8_25

Cited by 10 publications

(9 citation statements)

References 20 publications

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“…Some of them were described, for example, in [23]. In the present paper we use the so-called plain partitioning approach (in terms of [23]), that was used in [35,12,43,25], etc. Let us briefly describe it below.…”

Section: Black-box Optimization and Hard Sat Instancesmentioning

confidence: 99%

“…The only general-purpose optimization algorithm we have found so far which satisfies all three criteria is the Sequential Model-based Algorithm Configuration (SMAC) [21]. Also in paper [25] there was described the ALIAS tool which implements a variant of Greedy Best First Search (GBFS) algorithm designed specifically for minimization of function (1). Of course, it is also possible to implement specifically tailored versions of evolutionary or genetic algorithms [41] for this problem, as well as variable neighborhood search [17] or tabu search [15], as well as a number of other heuristics.…”

Section: Optimization Algorithms Involvedmentioning

confidence: 99%

“…As such algorithm, the Greedy Best First Search (GBFS) algorithm [34] was chosen. The GBFS algorithm described below was implemented as a module of the ALIAS tool [25].…”

Section: Greedy Best First Search In Aliasmentioning

confidence: 99%

“…When a local minimum is reached, in [25] the following simple jump strategy is used: the point with the current best-known value is partially randomly permuted and the obtained point is used as a new starting point. The algorithm stops either if the time limit is exceeded, or if the limit on jumps is reached.…”

Section: Greedy Best First Search In Aliasmentioning

confidence: 99%

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Pseudo-Boolean Black-Box Optimization Methods in the Context of Divide-and-Conquer Approach to Solving Hard SAT Instances

Zaikin¹,

Kochemazov²

2019

dtcse

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Solving hard instances of the Boolean satisfiability problem (SAT) in practice is an interestingly nontrivial area. In particular, the heuristic nature of state-of-the-art SAT solvers makes it impossible to know in advance how long it will take to solve any particular SAT instance. One way of coping with this disadvantage is the divide-and-conquer approach when an original SAT instance is decomposed into several simpler subproblems. However, the way it is decomposed plays a crucial role in the resulting effectiveness of solving. In the present study, we reduce the problem of "dividing" a hard SAT instance into many simpler subproblems to a pseudo-Boolean black-box optimization problem. Then we use relevant implementations of corresponding methods to analyze hard SAT instances encoding the cryptanalysis of state-of-the-art stream ciphers proposed during the recent e-STREAM competition.

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Section: Black-box Optimization and Hard Sat Instancesmentioning

confidence: 99%

Section: Optimization Algorithms Involvedmentioning

confidence: 99%

“…As such algorithm, the Greedy Best First Search (GBFS) algorithm [34] was chosen. The GBFS algorithm described below was implemented as a module of the ALIAS tool [25].…”

Section: Greedy Best First Search In Aliasmentioning

confidence: 99%

Section: Greedy Best First Search In Aliasmentioning

confidence: 99%

See 2 more Smart Citations

Pseudo-Boolean Black-Box Optimization Methods in the Context of Divide-and-Conquer Approach to Solving Hard SAT Instances

Zaikin¹,

Kochemazov²

2019

dtcse

Self Cite

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show abstract

“…и ALIAS[10], которые запускались на кластере ИДСТУ СО РАН Академик В. М. Матросов (http://hpc.icc.ru/). Таблица содержит оценки трудоёмкости (в секундах) для атак, использующих стандартную процедуру кодирования в SAT (отмечена как Standard) и процедуру, представленную в настоящей работе (отмечена как Middle).…”

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