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Dive into the research topics where Fernando Sánchez Villaamil is active.

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Featured researches published by Fernando Sánchez Villaamil.


european symposium on algorithms | 2013

Kernelization Using Structural Parameters on Sparse Graph Classes

Jakub Gajarský; Petr Hliněný; Jan Obdržálek; Sebastian Ordyniak; Felix Reidl; Peter Rossmanith; Fernando Sánchez Villaamil; Somnath Sikdar

Meta-theorems for polynomial (linear) kernels have been the subject of intensive research in parameterized complexity. Heretofore, there were meta-theorems for linear kernels on graphs of bounded genus, H-minor-free graphs, and H-topological-minor-free graphs. To the best of our knowledge, there are no known meta-theorems for kernels for any of the larger sparse graph classes: graphs of bounded expansion, locally bounded expansion, and nowhere dense graphs. In this paper we prove meta-theorems for these three graph classes. More specifically, we show that graph problems that have finite integer index (FII) admit linear kernels on hereditary graphs of bounded expansion when parameterized by the size of a modulator to constant-treedepth graphs. For hereditary graph classes of locally bounded expansion, our result yields a quadratic kernel and for hereditary nowhere dense graphs, a polynomial kernel. While our parameter may seem rather strong, a linear kernel result on graphs of bounded expansion with a weaker parameter would for some problems violate known lower bounds. Moreover, we use a relaxed notion of FII which allows us to prove linear kernels for problems such as Longest Path/Cycle and Exact s,t-Path which do not have FII in general graphs.


symposium on theoretical aspects of computer science | 2016

Kernelization and Sparseness: the Case of Dominating Set

Pål Grønås Drange; Markus Sortland Dregi; Fedor V. Fomin; Stephan Kreutzer; Daniel Lokshtanov; Marcin Pilipczuk; Michał Pilipczuk; Felix Reidl; Fernando Sánchez Villaamil; Saket Saurabh; Sebastian Siebertz; Somnath Sikdar

We prove that for every positive integer


international colloquium on automata, languages and programming | 2014

A Faster Parameterized Algorithm for Treedepth

Felix Reidl; Peter Rossmanith; Fernando Sánchez Villaamil; Somnath Sikdar

r


Journal of Computer and System Sciences | 2017

Kernelization using structural parameters on sparse graph classes

Jakub Gajarský; Petr Hlinźný; Jan Obdrźálek; Sebastian Ordyniak; Felix Reidl; Peter Rossmanith; Fernando Sánchez Villaamil; Somnath Sikdar

and for every graph class


European Journal of Combinatorics | 2019

Characterising bounded expansion by neighbourhood complexity

Felix Reidl; Fernando Sánchez Villaamil; Konstantinos Stavropoulos

\mathcal G


workshop on algorithms and models for the web graph | 2015

Hyperbolicity, Degeneracy, and Expansion of Random Intersection Graphs

Matthew Farrell; Timothy D. Goodrich; Nathan Lemons; Felix Reidl; Fernando Sánchez Villaamil; Blair D. Sullivan

of bounded expansion, the


statistical and scientific database management | 2011

Hierarchical clustering for real-time stream data with noise

Philipp Kranen; Felix Reidl; Fernando Sánchez Villaamil; Thomas Seidl

r


international symposium on parameterized and exact computation | 2015

Fast biclustering by dual parameterization

Pål Grønås Drange; Felix Reidl; Fernando Sánchez Villaamil; Somnath Sikdar

-Dominating Set problem admits a linear kernel on graphs from


Algorithms | 2018

Width, Depth, and Space: Tradeoffs between Branching and Dynamic Programming

Li-Hsuan Chen; Felix Reidl; Peter Rossmanith; Fernando Sánchez Villaamil

\mathcal G


international symposium on parameterized and exact computation | 2014

Finite Integer Index of Pathwidth and Treewidth

Jakub Gajarský; Jan Obdržálek; Sebastian Ordyniak; Felix Reidl; Peter Rossmanith; Fernando Sánchez Villaamil; Somnath Sikdar

. Moreover, when

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Felix Reidl

RWTH Aachen University

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Blair D. Sullivan

North Carolina State University

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Sebastian Ordyniak

Vienna University of Technology

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Erik D. Demaine

Massachusetts Institute of Technology

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Li-Hsuan Chen

National Chung Cheng University

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