Discovery Early Career Researcher Award - Grant ID: DE200100063

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DE200100063

Funding Status
Active

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Funded Activity Summary

Nonmonotone Algorithms in Operator Splitting, Optimisation and Data Science. This project aims to develop the mathematical foundations for the analysis and development of optimisation algorithms used in data science. Despite their now ubiquitous use, machine learning software packages routinely rely on a number of algorithms from mathematical optimisation which are not properly understood. By moving beyond the traditional realms of Fejér monotone algorithms, this project expects to develop the mathematical theory required to rigorously justify the use of such algorithms and thereby ensure the integrity of the decision tools they produce. This mathematical framework is also expected to produce new algorithms for optimisation which benefit consumers of data science such as the health-care and cybersecurity sectors.

Funded Activity Details

Start Date: 04-06-2020

End Date: 09-09-2023

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $394,398.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Optimisation | Applied Mathematics | Operations Research | Neural, Evolutionary and Fuzzy Computation |

ANZSRC Socio-Economic Objective (SEO)

Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in the Mathematical Sciences

Other Keywords

Applied Mathematics | Expanding Knowledge in the Information and Computing Sciences | Expanding Knowledge in the Mathematical Sciences | Neural, Evolutionary and Fuzzy Computation | Operations Research | Optimisation

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