METAKER

New Directions in Meta-Kernelization

 Coordinatore BEN-GURION UNIVERSITY OF THE NEGEV 

 Organization address address: Office of the President - Main Campus
city: BEER SHEVA
postcode: 84105

contact info
Titolo: Ms.
Nome: Daphna
Cognome: Tripto
Email: send email
Telefono: 97286472425
Fax: 97286472930

 Nazionalità Coordinatore Israel [IL]
 Totale costo 100˙000 €
 EC contributo 100˙000 €
 Programma FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call FP7-PEOPLE-2013-CIG
 Funding Scheme MC-CIG
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-03-01   -   2018-02-28

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    BEN-GURION UNIVERSITY OF THE NEGEV

 Organization address address: Office of the President - Main Campus
city: BEER SHEVA
postcode: 84105

contact info
Titolo: Ms.
Nome: Daphna
Cognome: Tripto
Email: send email
Telefono: 97286472425
Fax: 97286472930

IL (BEER SHEVA) coordinator 100˙000.00

Mappa


 Word cloud

Esplora la "nuvola delle parole (Word Cloud) per avere un'idea di massima del progetto.

sparse    structural    complexity    problem    preprocessing    graphs    kernels    parameterized    class    kernelization    instances    meta    parameterizations    techniques    algorithms   

 Obiettivo del progetto (Objective)

'Preprocessing for the purpose of simplifying problem instances is a universal algorithmic technique applied in almost every software implementation. Understanding and assessing preprocessing techniques is therefore of crucial practical importance in computer science. Kernelization is a notion developed in the area of parameterized complexity that provides a the only known reasonable mathematical model for analyzing preprocessing of NP-hard problems. Kernelization algorithms (or kernels) are polynomial time procedures which produce equivalent instances of a given problem whose sizes are bounded by some problem specific parameter which is associated with the inputs. These type of algorithms have become the central research focus of the parameterized complexity community, with many papers on the topic appearing each year, and an annual international workshop devoted entirely to them. Of particular interest are so-called meta-kernels, who are single preprocessing algorithms that apply to a multitude of problems simultaneously.

The current range of applicability of meta-kernelization is rather limited, and is restricted to certain subclasses of sparse graphs. This research proposal aims at remedying this situation by exploring new application domains for meta-kernelization. In doing so, we will explore new methods for obtaining such results, as well as obtain a better understanding of the limitations of current techniques. We suggest to look at three new directions:

1. The class of degenerate graphs which includes within it all classes of graphs for which meta-kernelization is currently known.

2. The class of claw-free graphs which is a non-sparse graph class, and whose structure has been recently unraveled due to the Chudnovsky-Seymour theory.

3. Meta-kernelization for structural parameterizations which measure structural aspects of the input rather than the solution size measured in standard parameterizations.'

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