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Found 18 papers, 2 papers with code
The Computational Complexity of Concise Hypersphere Classification
Hypersphere classification is a classical and foundational method that can provide easy-to-process explanations for the classification of real-valued and binary data.
Co-Certificate Learning with SAT Modulo Symmetries
We present a new SAT-based method for generating all graphs up to isomorphism that satisfy a given co-NP property.
Threshold Treewidth and Hypertree Width
However, here the order of the polynomial in the running time depends on the width, and this is known to be unavoidable; therefore, the problem is not fixed-parameter tractable parameterized by either of these width measures.
Are Hitting Formulas Hard for Resolution?
We show that the resolution complexity of hitting formulas is dominated by so-called irreducible hitting formulas, first studied by Kullmann and Zhao, that cannot be composed of smaller hitting formulas.
Learning Fast-Inference Bayesian Networks
We propose new methods for learning Bayesian networks (BNs) that reliably support fast inference.
A Time Leap Challenge for SAT Solving
We compare the impact of hardware advancement and algorithm advancement for SAT solving over the last two decades.
Turbocharging Treewidth-Bounded Bayesian Network Structure Learning
We present a new approach for learning the structure of a treewidth-bounded Bayesian Network (BN).
The Parameterized Complexity of Cascading Portfolio Scheduling
Cascading portfolio scheduling is a static algorithm selection strategy which uses a sample of test instances to compute an optimal ordering (a cascading schedule) of a portfolio of available algorithms.
Backdoors into Heterogeneous Classes of SAT and CSP
In this paper we extend the classical notion of strong and weak backdoor sets for SAT and CSP by allowing that different instantiations of the backdoor variables result in instances that belong to different base classes; the union of the base classes forms a heterogeneous base class.
Guarantees and Limits of Preprocessing in Constraint Satisfaction and Reasoning
We show that for most of the considered problems, task (i) admits a polynomial-time preprocessing to a problem kernel whose size is polynomial in a structural problem parameter of the input, in contrast to task (ii) which does not admit such a reduction to a problem kernel of polynomial size, subject to a complexity theoretic assumption.
The Complexity of Repairing, Adjusting, and Aggregating of Extensions in Abstract Argumentation
We study the computational complexity of problems that arise in abstract argumentation in the context of dynamic argumentation, minimal change, and aggregation.
Parameterized Complexity Results for Exact Bayesian Network Structure Learning
Furthermore, we show that if the directed super-structure is acyclic, then exact Bayesian network structure learning can be carried out in quadratic time.
A Complete Parameterized Complexity Analysis of Bounded Planning
The propositional planning problem is a notoriously difficult computational problem, which remains hard even under strong syntactical and structural restrictions.
Parameterized Complexity Results for Plan Reuse
We perform our analysis in the framework of parameterized complexity, which supports a rigorous worst-case complexity analysis that takes structural properties of the input into account in terms of parameters.
Backdoors to Abduction
This complexity barrier rules out the existence of a polynomial transformation to propositional satisfiability (SAT).
Local Backbones
The empirical results we obtain show that a large fraction of the backbones of structured SAT instances are local, in contrast to random instances, which appear to have few local backbones.
Backdoors to Normality for Disjunctive Logic Programs
One cannot transform these two reasoning problems into SAT in polynomial time, unless the Polynomial Hierarchy collapses.
Backdoors to Tractable Answer-Set Programming
We demonstrate how backdoors can serve as a unifying framework that accommodates several tractable restrictions of ASP known from the literature.
