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Research Topic : Approximation Theory
Australian State/Territory : ACT
Status : Closed
Field of Research : Optimisation
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  • Funded Activity

    ARC Future Fellowships - Grant ID: FT100100538

    Funder
    Australian Research Council
    Funding Amount
    $700,590.00
    Summary
    Low emission road transportation: harnessing the potential of alternative fuels and advanced vehicle technologies through online optimisation. This project will develop fundamental mathematical theory and use it to enable the best possible CO2 reduction capability in road vehicles. The cost of different technologies and fuels will then be compared to determine the most cost effective approaches to reduce road transport emissions.
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    Funded Activity

    Discovery Projects - Grant ID: DP160103489

    Funder
    Australian Research Council
    Funding Amount
    $343,700.00
    Summary
    Frontiers in inference about risk. The project aims to develop new methods for robust risk evaluation and minimisation under various constraints and scenarios. Risk evaluation, estimation and prediction using past data is a central activity in diverse areas such as finance, insurance, superannuation and environmental regulation. The project aims to propose and solve innovatively robust risk optimisation problems under constraints, taking into account the time dynamics. Applications include risk .... Frontiers in inference about risk. The project aims to develop new methods for robust risk evaluation and minimisation under various constraints and scenarios. Risk evaluation, estimation and prediction using past data is a central activity in diverse areas such as finance, insurance, superannuation and environmental regulation. The project aims to propose and solve innovatively robust risk optimisation problems under constraints, taking into account the time dynamics. Applications include risk management around natural catastrophes and long-term asset investment of pension funds. The solutions and outcomes are expected to deliver optimal resource allocation proposals and better management of risk exposure in practice.
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    Funded Activity

    Discovery Projects - Grant ID: DP0774054

    Funder
    Australian Research Council
    Funding Amount
    $263,993.00
    Summary
    Unifying Modern Approaches in Machine Learning. The proposed research will lead to better algorithms for some important machine learning problems that could lead to better tools for extracting useful knowledge from data such as in bioinformatics and sensor networks; it will strengthen an international collaboration with one of the world's top centres of machine learning research; it will contribute to an open source toolkit of machine learning algorithms which will put Australia on the map as a .... Unifying Modern Approaches in Machine Learning. The proposed research will lead to better algorithms for some important machine learning problems that could lead to better tools for extracting useful knowledge from data such as in bioinformatics and sensor networks; it will strengthen an international collaboration with one of the world's top centres of machine learning research; it will contribute to an open source toolkit of machine learning algorithms which will put Australia on the map as a provider of sophisticated machine learning software; it will provide training opportunities for several PhD students and a postdoc to work with some of the best machine learning researchers in the world.
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    Funded Activity

    Discovery Projects - Grant ID: DP0450539

    Funder
    Australian Research Council
    Funding Amount
    $243,966.00
    Summary
    Numerical Algorithms for Solving Convex Optimization Problems Arising in Systems and Control Theory. The need to optimize occurs frequently in engineering applications. Typically one has a set of constraints specifying what solutions are allowable or meet design specifications and one would like to choose from these allowable solutions one which is optimal with respect to some meaningful metric. Such optimization problems tend to be rather complicated and must be solved numerically. This project .... Numerical Algorithms for Solving Convex Optimization Problems Arising in Systems and Control Theory. The need to optimize occurs frequently in engineering applications. Typically one has a set of constraints specifying what solutions are allowable or meet design specifications and one would like to choose from these allowable solutions one which is optimal with respect to some meaningful metric. Such optimization problems tend to be rather complicated and must be solved numerically. This project is concerned with creating improved numerical algorithms for solving particular important classes of optimization problems that arise in systems and control theory.
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    Showing 1-4 of 4 Funded Activites

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