ARC Future Fellowships - Grant ID: FT130100038

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/FT130100038

Funding Status
Closed

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Funded Activity Summary

A New Optimization Approach for Tensor Extreme Eigenvalue Problems: Modern Techniques for Multi-relational Data Analysis. Nowadays, we often encounter complex multi-relational data whose objects have interactions among themselves based on different relations. These multi-relational data can be mathematically modelled as tensors. The tensor extreme eigenvalue problem, which is concerned with extracting the most significant qualitative information from multi-relational data, plays a key role in modern data analysis. This project aims at developing innovative global optimisation frameworks and reliable numerical methods for tensor extreme eigenvalue problems, and applying the proposed methods to solve various practical problems arising from important application areas such as modern data analysis, medical imaging science and signal processing.

Funded Activity Details

Start Date: 06-01-2014

End Date: 31-12-2019

Funding Scheme: ARC Future Fellowships

Funding Amount: $606,300.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Calculus of Variations, Systems Theory and Control Theory | Optimisation | Numerical and Computational Mathematics |

ANZSRC Socio-Economic Objective (SEO)

Expanding Knowledge in the Mathematical Sciences

Other Keywords

Calculus of Variations, Systems Theory and Control Theory | Expanding Knowledge in the Mathematical Sciences | Numerical and Computational Mathematics | Optimisation

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