ACUITY

Algorithms for coping with uncertainty and intractability

 Coordinatore TECHNISCHE UNIVERSITEIT EINDHOVEN 

Spiacenti, non ci sono informazioni su questo coordinatore. Contattare Fabio per maggiori infomrazioni, grazie.

 Nazionalità Coordinatore Netherlands [NL]
 Totale costo 1˙519˙285 €
 EC contributo 1˙519˙285 €
 Programma FP7-IDEAS-ERC
Specific programme: "Ideas" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013)
 Code Call ERC-2013-CoG
 Funding Scheme ERC-CG
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-05-01   -   2019-04-30

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITEIT EINDHOVEN

 Organization address address: DEN DOLECH 2
city: EINDHOVEN
postcode: 5612 AZ

contact info
Titolo: Dr.
Nome: Nikhil
Cognome: Bansal
Email: send email
Telefono: +31 40 247 2299
Fax: +31 40 2436685

NL (EINDHOVEN) hostInstitution 1˙519˙285.00
2    TECHNISCHE UNIVERSITEIT EINDHOVEN

 Organization address address: DEN DOLECH 2
city: EINDHOVEN
postcode: 5612 AZ

contact info
Titolo: Mr.
Nome: Patrick
Cognome: Groothuis
Email: send email
Telefono: +31 40 2475165
Fax: +31 40 2436685

NL (EINDHOVEN) hostInstitution 1˙519˙285.00

Mappa


 Word cloud

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optimization    online    constraints    hard    directions    progress    approximation    techniques    theory    algorithmic    uncertainty    intractability    algorithms   

 Obiettivo del progetto (Objective)

'The two biggest challenges in solving practical optimization problems are computational intractability, and the presence of uncertainty: most problems are either NP-hard, or have incomplete input data which makes an exact computation impossible.

Recently, there has been a huge progress in our understanding of intractability, based on spectacular algorithmic and lower bound techniques. For several problems, especially those with only local constraints, we can design optimum approximation algorithms that are provably the best possible. However, typical optimization problems usually involve complex global constraints and are much less understood. The situation is even worse for coping with uncertainty. Most of the algorithms are based on ad-hoc techniques and there is no deeper understanding of what makes various problems easy or hard.

This proposal describes several new directions, together with concrete intermediate goals, that will break important new ground in the theory of approximation and online algorithms. The particular directions we consider are (i) extend the primal dual method to systematically design online algorithms, (ii) build a structural theory of online problems based on work functions, (iii) develop new tools to use the power of strong convex relaxations and (iv) design new algorithmic approaches based on non-constructive proof techniques.

The proposed research is at the cutting edge of algorithm design, and builds upon the recent success of the PI in resolving several longstanding questions in these areas. Any progress is likely to be a significant contribution to theoretical computer science and combinatorial optimization.'

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