Opendata, web and dolomites

ReduceSearch SIGNED

Rigorous Search Space Reduction

Total Cost €

0

EC-Contrib. €

0

Partnership

0

Views

0

Project "ReduceSearch" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE UNIVERSITEIT EINDHOVEN 

Organization address
address: GROENE LOPER 3
city: EINDHOVEN
postcode: 5612 AE
website: www.tue.nl/en

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Netherlands [NL]
 Total cost 1˙473˙020 €
 EC max contribution 1˙473˙020 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2018-STG
 Funding Scheme ERC-STG
 Starting year 2019
 Duration (year-month-day) from 2019-01-01   to  2023-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITEIT EINDHOVEN NL (EINDHOVEN) coordinator 1˙473˙020.00

Map

 Project objective

In our world of big data and theoretically intractable problems, automated preprocessing to simplify problem formulations before solving them algorithmically is growing ever more important. Suitable preprocessing has the potential to reduce computation times from days to seconds. In the last 15 years, a framework for rigorously studying the power and limitations of efficient preprocessing has been developed. The resulting theory of kernelization is full of deep theorems, but it has overshot its goal: it does not explain the empirical success of preprocessing algorithms, and most questions it poses do not lead to the identification of preprocessing techniques that are useful in practice. This crucial flaw stems from the fact that the theoretical kernelization framework does not address the main experimentally observed cause of algorithmic speed-ups: a reduction in the search space of the subsequently applied problem-solving algorithm.

REDUCESEARCH will re-shape the theory of effective preprocessing with a focus on search-space reduction. The goal is to develop a toolkit of algorithmic preprocessing techniques that reduce the search space, along with rigorous mathematical guarantees on the amount of search-space reduction that is achieved in terms of quantifiable properties of the input. The three main algorithmic strategies are: (1) reducing the size of the solution that the solver has to find, by already identifying parts of the solution during the preprocessing phase; (2) splitting the search space into parts with limited interaction, which can be solved independently; and (3) identifying redundant constraints and variables in a problem formulation, which can be eliminated without changing the answer.

This will raise the scientific study of preprocessing to the next level. Since physical limits form a barrier to further speeding up computer hardware, future advances in computing power rely on algorithmic breakthroughs as envisioned here.

Are you the coordinator (or a participant) of this project? Plaese send me more information about the "REDUCESEARCH" project.

For instance: the website url (it has not provided by EU-opendata yet), the logo, a more detailed description of the project (in plain text as a rtf file or a word file), some pictures (as picture files, not embedded into any word file), twitter account, linkedin page, etc.

Send me an  email (fabio@fabiodisconzi.com) and I put them in your project's page as son as possible.

Thanks. And then put a link of this page into your project's website.

The information about "REDUCESEARCH" are provided by the European Opendata Portal: CORDIS opendata.

More projects from the same programme (H2020-EU.1.1.)

DOUBLE-TROUBLE (2020)

Replaying the ‘genome duplication’ tape of life: the importance of polyploidy for adaptation in a changing environment

Read More  

Growth regulation (2019)

The wide-spread bacterial toxin delivery systems and their role in multicellularity

Read More  

E-DIRECT (2020)

Evolution of Direct Reciprocity in Complex Environments

Read More