OPT-GEOM-RS

Optimization Problems on Geometric Range Spaces

 Coordinatore TEL AVIV UNIVERSITY 

 Organization address address: RAMAT AVIV
city: TEL AVIV
postcode: 69978

contact info
Titolo: Ms.
Nome: Lea
Cognome: Pais
Email: send email
Telefono: 97236408774
Fax: 97236409697

 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-2012-CIG
 Funding Scheme MC-CIG
 Anno di inizio 2014
 Periodo (anno-mese-giorno) 2014-09-01   -   2018-08-31

 Partecipanti

# participant  country  role  EC contrib. [€] 
1    TEL AVIV UNIVERSITY

 Organization address address: RAMAT AVIV
city: TEL AVIV
postcode: 69978

contact info
Titolo: Ms.
Nome: Lea
Cognome: Pais
Email: send email
Telefono: 97236408774
Fax: 97236409697

IL (TEL AVIV) coordinator 100˙000.00

Mappa


 Word cloud

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

tools    sensor    computational    abstract    suggests    solutions    optimum    learning    front    optimization    solution    networking    theoretical    structure    algorithms    geometry    geometric    mathematical    computer    fact    objects   

 Obiettivo del progetto (Objective)

'Computational geometry is a subfield of theoretical computer science devoted to the design and implementation of geometric algorithms, as well as to their analysis, and to the combinatorial structure that they manipulate. In particular, computational geometry encompasses a diversity of optimization problems. It is often infeasible in practice to find the optimal solution of a geometric optimization problem. This is because the optimum might typically be hard to compute. This suggests that instead of insisting on computing the exact solution for optimization problems, one should be satisfied with a possibly suboptimal solution that approximates the optimum reasonably well.

The goal of this proposal is to consider optimization problems involving geometric objects in low dimensions, and design efficient algorithms that guarantee a good approximation for their solutions. Such problems have been well studied in abstract settings (that is, when the objects are abstract and no geometric properties are known), but their geometric variants have received much less attention, and the solutions to most of these problems have still remained elusive.

This project suggests an interdisciplinary challenge. On the theoretical front, it aims to develop a set of mathematical tools taken from discrete geometry, such as geometric arrangements and epsilon nets, which exploit the geometric structure of the given setting. In fact, such tools can be exploited on a broader set of problems, and lie beyond the scope of the problems presented in this proposal. On the applied front, this problems have applications to other disciplines as sensor networking, computer graphics, geographic information systems, machine learning and more. In fact, the PI is collaborating with researchers from sensor networking and learning where she applies such mathematical tools in order to solve problems from the real world.'

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