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TENSOROOTS TERMINATED

Solving large systems of polynomial equations by using multi-linear algebra tools

Total Cost €

0

EC-Contrib. €

0

Partnership

0

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Project "TENSOROOTS" data sheet

The following table provides information about the project.

Coordinator
KATHOLIEKE UNIVERSITEIT LEUVEN 

Organization address
address: OUDE MARKT 13
city: LEUVEN
postcode: 3000
website: www.kuleuven.be

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 Belgium [BE]
 Total cost 160˙800 €
 EC max contribution 160˙800 € (100%)
 Programme 1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
 Code Call H2020-MSCA-IF-2016
 Funding Scheme MSCA-IF-EF-ST
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2019-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) coordinator 160˙800.00

Map

 Project objective

The modelling of real-life nonlinear processes is commonly done via local linearization, i.e., by fitting linear models to small ranges of the process. Linearization is, however, quite limited in range and model properties. A foundational step is currently being made by moving from linear to multi-linear or polynomial models to capture more general features of a nonlinear process over larger ranges. A second important development is Big Data: nowadays we often encounter high-dimensional datasets with multiple `independent’ modes of variation. This increases the numbers of equations and variables and the degrees of the polynomials in the multi-linear models that are required. Big Data research is a key priority in the EU Horizon 2020 Work Programme.

This proposal joins both the cutting edge multi-linear and Big Data developments and as a next step aims to develop new robust and efficient numerical methods to solve large polynomial systems. Polynomial equations arise naturally in a large number of diverse fields such as signal processing, robotics, coding theory, optimization, bioinformatics, computer vision, game theory, statistics, machine learning, and systems and control theory. In many applications the coefficients of the polynomials are noisy (i.e., computed from measured data). Currently, solving polynomial equations is commonly done via ‘symbolic algebra’ tools, but these are not suitable for equations with noisy coefficients. However, the rediscovered work of Sylvester and Macaulay puts the problem in a linear algebra setting and well-known numerical linear algebra tools can be used. This project will go beyond this and uses new highly efficient multi-linear algebra tools in the field of tensor decompositions. The latter are higher-order generalizations of the matrix singular valued decomposition.

The project combines polynomial algebra, numerical linear algebra, and multi-linear algebra, and will have large impact on a large number of applications.

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

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