Worst-case Complexity of Exact Algorithms forNP-hard Problems

2010 ◽  
pp. 203-240
Author(s):  
Federico Della Croce ◽  
Bruno Escoffier ◽  
Marcin Kamiski ◽  
Vangelis Th. Paschos
Keyword(s):  
Exact Algorithms ◽  
Worst Case ◽  
Case Complexity ◽  
Worst Case Complexity ◽  
Hard Problems

scholarly journals On the optimal order of worst case complexity of direct search

2015 ◽  
Vol 10 (4) ◽  
pp. 699-708 ◽  
Author(s):  
M. Dodangeh ◽  
L. N. Vicente ◽  
Z. Zhang
Keyword(s):  
Direct Search ◽  
Optimal Order ◽  
Worst Case ◽  
Case Complexity ◽  
Worst Case Complexity

scholarly journals Ontology-Mediated Queries from Examples: a Glimpse at the DL-Lite Case

10.29007/jhtz ◽  
2019 ◽  
Author(s):  
Magdalena Ortiz
Keyword(s):  
Artificial Intelligence ◽  
Reverse Engineering ◽  
Worst Case ◽  
Research Directions ◽  
Conjunctive Queries ◽  
Query By Example ◽  
Case Complexity ◽  
Worst Case Complexity

Reverse engineering queries from given data, as in the case of query-by-example and query definability, is an important problem with many applications that has recently gained attention in the areas where symbolic artificial intelligence meets learning. In the presence of ontologies this problem was recently studied for Horn-ALC and Horn-ALCI. The main contribution of this paper is to take a first look at the case of DL-Lite, to identify cases where the addition of the ontology does not increase the worst-case complexity of the problem. Unfortunately, reverse engineering conjunctive queries is known to be very hard, even for plain databases, since the smallest witness query is known to be exponential in general. In the light of this, we outline some possible research directions for exploiting the ontology in order to obtain smaller witness queries.

scholarly journals Solving Mean-Payoff Games via Quasi Dominions

2020 ◽  
pp. 289-306
Author(s):  
Massimo Benerecetti ◽  
Daniele Dell’Erba ◽  
Fabio Mogavero
Keyword(s):  
Solution Algorithm ◽  
Problem Solution ◽  
Worst Case ◽  
Case Complexity ◽  
Worst Case Complexity ◽  
Mean Payoff Games ◽  
Parity Games ◽  
Mean Payoff ◽  
Novel Algorithm ◽  
Better Than

Abstract We propose a novel algorithm for the solution of mean-payoff games that merges together two seemingly unrelated concepts introduced in the context of parity games, small progress measures and quasi dominions. We show that the integration of the two notions can be highly beneficial and significantly speeds up convergence to the problem solution. Experiments show that the resulting algorithm performs orders of magnitude better than the asymptotically-best solution algorithm currently known, without sacrificing on the worst-case complexity.

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