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Sampling-based motion planning algorithms are widely used in robotics because they are very effective in high-dimensional spaces. However, the success rate and quality of the solutions are determined by an adequate selection of their…

We study a class of random 3-SAT instances having exactly one solution. The properties of this ensemble considerably differ from those of a random 3-SAT ensemble. It is numerically shown that the running time of several complete and…

Artificial Intelligence · Computer Science 2007-05-23 Marko Znidaric

The runtime performance of modern SAT solvers on random $k$-CNF formulas is deeply connected with the 'phase-transition' phenomenon seen empirically in the satisfiability of random $k$-CNF formulas. Recent universal hashing-based approaches…

Discrete Mathematics · Computer Science 2017-02-28 Jeffrey M. Dudek , Kuldeep S. Meel , Moshe Y. Vardi

Maximum Satisfiability (MaxSAT) is an optimization variant of the Boolean Satisfiability (SAT) problem. In general, MaxSAT algorithms perform a succession of SAT solver calls to reach an optimum solution making extensive use of cardinality…

Logic in Computer Science · Computer Science 2014-08-21 Ruben Martins , Saurabh Joshi , Vasco Manquinho , Ines Lynce

Applying pre- and inprocessing techniques to simplify CNF formulas both before and during search can considerably improve the performance of modern SAT solvers. These algorithms mostly aim at reducing the number of clauses, literals, and…

Logic in Computer Science · Computer Science 2013-10-18 Andreas Wotzlaw , Alexander van der Grinten , Ewald Speckenmeyer

Stochastic approximation (SA) algorithms are widely used in system optimization problems when only noisy measurements of the system are available. This paper studies two types of SA algorithms in a multivariate Kiefer-Wolfowitz setting:…

Optimization and Control · Mathematics 2021-07-28 Yiwen Chen

Ranking algorithms are deployed widely to order a set of items in applications such as search engines, news feeds, and recommendation systems. Recent studies, however, have shown that, left unchecked, the output of ranking algorithms can…

Data Structures and Algorithms · Computer Science 2018-07-31 L. Elisa Celis , Damian Straszak , Nisheeth K. Vishnoi

There is no known polynomial-time algorithm that can solve an NP problem. Evolutionary search has been shown to be a viable method of finding acceptable solutions within a reasonable time period. Recently quantum computers have surfaced as…

Quantum Physics · Physics 2007-05-23 G. W. Greenwood

We consider constraint satisfaction problems parameterized above or below tight bounds. One example is MaxSat parameterized above $m/2$: given a CNF formula $F$ with $m$ clauses, decide whether there is a truth assignment that satisfies at…

Data Structures and Algorithms · Computer Science 2011-08-25 G. Gutin , A. Yeo

The importance of algorithm portfolio techniques for SAT has long been noted, and a number of very successful systems have been devised, including the most successful one --- SATzilla. However, all these systems are quite complex (to…

Artificial Intelligence · Computer Science 2011-12-14 Mladen Nikolic , Filip Maric , Predrag Janicic

It has been hypothesized that $k$-SAT is hard to solve for randomly chosen instances near the "critical threshold", where the clause-to-variable ratio is $2^k \ln 2-\theta(1)$. Feige's hypothesis for $k$-SAT says that for all sufficiently…

Data Structures and Algorithms · Computer Science 2018-10-16 Nikhil Vyas

Boolean satisfiability (SAT) problem is of fundamental importance in computer science and many application domains. For Grover's algorithm, solving the SAT problem requires $\mathcal{O}(\sqrt{2^n})$ queries--where n denotes the number of…

Quantum Physics · Physics 2026-04-14 He Wang , Jinyang Yao

We propose a resource-constrained heuristic for instances of Max-SAT that iteratively decomposes a larger problem into smaller subcomponents that can be solved by optimized solvers and hardware. The unconstrained outer loop maintains the…

Artificial Intelligence · Computer Science 2024-10-15 Brian Matejek , Daniel Elenius , Cale Gentry , David Stoker , Adam Cobb

We propose an incomplete algorithm for Maximum Satisfiability (MaxSAT) specifically designed to run on neural network accelerators such as GPUs and TPUs. Given a MaxSAT problem instance in conjunctive normal form, our procedure constructs a…

Artificial Intelligence · Computer Science 2023-11-07 David Warde-Farley , Vinod Nair , Yujia Li , Ivan Lobov , Felix Gimeno , Simon Osindero

We present an analysis of sets of matrices with rank less than or equal to a specified number $s$. We provide a simple formula for the normal cone to such sets, and use this to show that these sets are prox-regular at all points with rank…

Optimization and Control · Mathematics 2018-09-24 D. Russell Luke

Finding a low-weight multiple (LWPM) of a given polynomial is very useful in the cryptanalysis of stream ciphers and arithmetic in finite fields. There is no known deterministic polynomial time complexity algorithm for solving this problem,…

Computational Complexity · Computer Science 2024-10-15 Ferucio Laurenţiu Ţiplea , Simona-Maria Lăzărescu

Survey propagation is a powerful technique from statistical physics that has been applied to solve the 3-SAT problem both in principle and in practice. We give, using only probability arguments, a common derivation of survey propagation,…

Statistical Mechanics · Physics 2007-05-23 Erik Aurell , Uri Gordon , Scott Kirkpatrick

We here study Max Hamming XSAT, ie, the problem of finding two XSAT models at maximum Hamming distance. By using a recent XSAT solver as an auxiliary function, an O(1.911^n) time algorithm can be constructed, where n is the number of…

Data Structures and Algorithms · Computer Science 2007-05-23 Vilhelm Dahllof

We introduce a diversified top-k partial MaxSAT problem, a combination of partial MaxSAT problem and enumeration problem. Given a partial MaxSAT formula F and a positive integer k, the diversified top-k partial MaxSAT is to find k maximal…

Artificial Intelligence · Computer Science 2017-06-02 Junping Zhou , Huanyao Sun , Feifei Ma , Jian Gao , Ke Xu , Minghao Yin

We study randomized algorithms for constrained optimization, in abstract frameworks that include, in strictly increasing generality: convex programming; LP-type problems; violator spaces; and a setting we introduce, consistent spaces. Such…

Computational Geometry · Computer Science 2019-06-04 Kenneth L. Clarkson , Bernd Gärtner , Johannes Lengler , May Szedlak
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