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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

Many recent algorithms for approximate model counting are based on a reduction to combinatorial searches over random subsets of the space defined by parity or XOR constraints. Long parity constraints (involving many variables) provide…

Computational Complexity · Computer Science 2016-09-12 Shengjia Zhao , Sorathan Chaturapruek , Ashish Sabharwal , Stefano Ermon

An NP-hard combinatorial optimization problem $\Pi$ is said to have an {\em approximation threshold} if there is some $t$ such that the optimal value of $\Pi$ can be approximated in polynomial time within a ratio of $t$, and it is NP-hard…

Computational Complexity · Computer Science 2008-12-15 Uriel Feige

The constraint satisfaction problem (CSP) on a relational structure B is to decide, given a set of constraints on variables where the relations come from B, whether or not there is a assignment to the variables satisfying all of the…

Logic in Computer Science · Computer Science 2014-06-03 Hubie Chen

We study the Hardy inequality when the singularity is placed on the boundary of a bounded domain in $\mathbb{R}^n$ that satisfies both an interior and exterior ball condition at the singularity. We obtain the sharp Hardy constant $n^2/4$ in…

Analysis of PDEs · Mathematics 2018-04-06 Gerassimos Barbatis , Stathis Filippas , Achilles Tertikas

A classic result due to Schaefer (1978) classifies all constraint satisfaction problems (CSPs) over the Boolean domain as being either in $\mathsf{P}$ or $\mathsf{NP}$-hard. This paper considers a promise-problem variant of CSPs called…

Computational Complexity · Computer Science 2021-05-07 Joshua Brakensiek , Venkatesan Guruswami

In [Lavielle and Ludena 07], a random thresholding metho d is intro duced to select the significant, or non null, mean terms among a collection of independent random variables, and applied to the problem of recovering the significant…

Methodology · Statistics 2010-10-27 Merlin Keller , Marc Lavielle

While useful probability bounds for $n$ pairwise independent Bernoulli random variables adding up to at least an integer $k$ have been proposed in the literature, none of these bounds are tight in general. In this paper, we provide several…

Optimization and Control · Mathematics 2022-11-24 Arjun Ramachandra , Karthik Natarajan

One of the central problems in the study of parametrized constraint satisfaction problems is the Dichotomy Conjecture by T. Feder and M. Vardi stating that the constraint satisfaction problem (CSP) over a fixed, finite constraint language…

Computational Complexity · Computer Science 2017-12-12 Dejan Delić

This paper is devoted to the complexity of the Boolean satisfiability problem. We consider a version of this problem, where the Boolean formula is specified in the conjunctive normal form. We prove an unexpected result that the…

Computational Complexity · Computer Science 2018-07-23 Grigoriy V. Bokov

This article studies the achievable guarantees on the error rates of certain learning algorithms, with particular focus on refining logarithmic factors. Many of the results are based on a general technique for obtaining bounds on the error…

Machine Learning · Computer Science 2016-09-13 Steve Hanneke

We show sharp bounds for probabilities of large deviations for sums of independent random variables satisfying Bernstein's condition. One such bound is very close to the tail of the standard Gaussian law in certain case; other bounds…

Probability · Mathematics 2015-07-13 Xiequan Fan , Ion Grama , Quansheng Liu

Many constraint satisfaction and optimisation problems can be solved effectively by encoding them as instances of the Boolean Satisfiability problem (SAT). However, even the simplest types of constraints have many encodings in the…

Artificial Intelligence · Computer Science 2023-11-09 Felix Ulrich-Oltean , Peter Nightingale , James Alfred Walker

We study a tight Bennett-type concentration inequality for sums of heterogeneous and independent variables, defined as a one-dimensional minimization. We show that this refinement, which outperforms the standard known bounds, remains…

Optimization and Control · Mathematics 2022-11-23 Quentin Jacquet , Riadh Zorgati

We analyse the convergence of the proximal gradient algorithm for convex composite problems in the presence of gradient and proximal computational inaccuracies. We derive new tighter deterministic and probabilistic bounds that we use to…

Optimization and Control · Mathematics 2022-03-07 Anis Hamadouche , Yun Wu , Andrew M. Wallace , Joao F. C. Mota

In this work we introduce a novel approach, based on sampling, for finding assignments that are likely to be solutions to stochastic constraint satisfaction problems and constraint optimisation problems. Our approach reduces the size of the…

Optimization and Control · Mathematics 2015-09-22 Roberto Rossi , Brahim Hnich , S. Armagan Tarim , Steven Prestwich

Constraint Satisfaction Problems (CSP) constitute a convenient way to capture many combinatorial problems. The general CSP is known to be NP-complete, but its complexity depends on a template, usually a set of relations, upon which they are…

Computational Complexity · Computer Science 2010-11-23 Florian Richoux

The study of phase transition phenomenon of NP complete problems plays an important role in understanding the nature of hard problems. In this paper, we follow this line of research by considering the problem of counting solutions of…

Artificial Intelligence · Computer Science 2011-02-25 Minghao Yin , Ping Huang

We investigate the Constraint Satisfaction Problem (CSP) over templates with a group structure, and algorithms solving CSP that are equivariant, i.e. invariant under a natural group action induced by a template. Our main result is a method…

Logic in Computer Science · Computer Science 2016-04-06 Sławomir Lasota

We show that sparsity constrained optimization problems over low dimensional spaces tend to have a small duality gap. We use the Shapley-Folkman theorem to derive both data-driven bounds on the duality gap, and an efficient primalization…

Optimization and Control · Mathematics 2021-02-16 Armin Askari , Alexandre d'Aspremont , Laurent El Ghaoui
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