ARDC Research Link Australia Research Link Australia   BETA Research
Link
Australia
  • ARDC Newsletter Subscribe
  • Contact Us
  • Home
  • About
  • Feedback
  • Explore Collaborations
  • Researcher
  • Funded Activity
  • Organisation
  • Researcher
  • Funded Activity
  • Organisation
  • Researcher
  • Funded Activity
  • Organisation

Need help searching? View our Search Guide.

Advanced Search

Current Selection
Australian State/Territory : QLD
Research Topic : Numerical Analysis
Field of Research : Optimisation
Clear All
Filter by Field of Research
Optimisation (4)
Analysis of Algorithms and Complexity (1)
Artificial Intelligence and Image Processing (1)
Biological Mathematics (1)
Knowledge Representation and Machine Learning (1)
Numerical and Computational Mathematics (1)
Pure Mathematics (1)
Simulation and Modelling (1)
Statistics (1)
Stochastic Analysis And Modelling (1)
Stochastic Analysis and Modelling (1)
Topology (1)
Filter by Socio-Economic Objective
Expanding Knowledge in the Mathematical Sciences (3)
Application Software Packages (excl. Computer Games) (2)
Expanding Knowledge in the Information and Computing Sciences (2)
Application tools and system utilities (1)
Biological sciences (1)
Expanding Knowledge in the Earth Sciences (1)
Expanding Knowledge in the Physical Sciences (1)
Mathematical sciences (1)
Filter by Funding Provider
Australian Research Council (4)
Filter by Status
Closed (3)
Active (1)
Filter by Scheme
Discovery Projects (3)
Discovery Early Career Researcher Award (1)
Filter by Country
Australia (4)
Filter by Australian State/Territory
QLD (4)
VIC (1)
  • Researchers (8)
  • Funded Activities (4)
  • Organisations (2)
  • Active Funded Activity

    Discovery Early Career Researcher Award - Grant ID: DE180100923

    Funder
    Australian Research Council
    Funding Amount
    $348,575.00
    Summary
    Efficient second-order optimisation algorithms for learning from big data. This project aims to apply a diverse range of scientific computing techniques to design and implement new, second-order methods that can surpass first-order alternatives in the next generation of optimisation methods for large-scale machine learning (ML). Scalable optimisation methods are now an integral part ML in the presence of “big data”. While the development of efficient first-order methods has grown in the ML comm .... Efficient second-order optimisation algorithms for learning from big data. This project aims to apply a diverse range of scientific computing techniques to design and implement new, second-order methods that can surpass first-order alternatives in the next generation of optimisation methods for large-scale machine learning (ML). Scalable optimisation methods are now an integral part ML in the presence of “big data”. While the development of efficient first-order methods has grown in the ML community, second-order alternatives have largely been ignored. The project expects to facilitate the development of more effective ML algorithms for extraction of knowledge from large data sets.
    Read more Read less
    More information
    Funded Activity

    Discovery Projects - Grant ID: DP0556631

    Funder
    Australian Research Council
    Funding Amount
    $244,141.00
    Summary
    Cross-Entropy Methods in Complex Biological Systems. The Cross-Entropy method provides a powerful new way to find superior solutions to complicated optimisation problems in biology, ranging from better design and implementation of medical treatments to an increased understanding of complex ecosystems.
    More information
    Funded Activity

    Discovery Projects - Grant ID: DP140104246

    Funder
    Australian Research Council
    Funding Amount
    $350,000.00
    Summary
    Unlocking the potential for linear and discrete optimisation in knot theory and computational topology. Computational topology is a young, energetic field that uses computers to solve complex geometric problems, such as whether a loop of string is tangled. Such computations are becoming increasingly important in mathematics, and applications span biology, physics and information sciences, however many core problems in the field remain intractable for all but the simplest cases. This project unit .... Unlocking the potential for linear and discrete optimisation in knot theory and computational topology. Computational topology is a young, energetic field that uses computers to solve complex geometric problems, such as whether a loop of string is tangled. Such computations are becoming increasingly important in mathematics, and applications span biology, physics and information sciences, however many core problems in the field remain intractable for all but the simplest cases. This project unites geometric techniques with powerful methods from operations research, such as linear and discrete optimisation, to build fast, powerful tools that can for the first time systematically solve large topological problems. Theoretically, this project has significant impact on the famous open problem of detecting knottedness in fast polynomial time.
    Read more Read less
    More information
    Funded Activity

    Discovery Projects - Grant ID: DP140101956

    Funder
    Australian Research Council
    Funding Amount
    $280,000.00
    Summary
    Advanced Monte Carlo Methods for Spatial Processes. The modeling and analysis of spatial data relies more and more on sophisticated Monte Carlo simulation methods. However, with the growing complexity of today's spatial data, traditional Monte Carlo methods increasingly face difficulties in terms of speed and accuracy. The aim of this project is to develop new theory and applications at the interface of Monte Carlo methods and spatial statistics, building upon exciting theoretical and computatio .... Advanced Monte Carlo Methods for Spatial Processes. The modeling and analysis of spatial data relies more and more on sophisticated Monte Carlo simulation methods. However, with the growing complexity of today's spatial data, traditional Monte Carlo methods increasingly face difficulties in terms of speed and accuracy. The aim of this project is to develop new theory and applications at the interface of Monte Carlo methods and spatial statistics, building upon exciting theoretical and computational advances in both areas in recent years. The research will stimulate the design of microscopic and macroscopic complex spatial structures with superior properties, such as composite materials, solar cells, telecommunication networks, mining operations, and road systems.
    Read more Read less
    More information

    Showing 1-4 of 4 Funded Activites

    Advanced Search

    Advanced search on the Researcher index.

    Advanced search on the Funded Activity index.

    Advanced search on the Organisation index.

    National Collaborative Research Infrastructure Strategy

    The Australian Research Data Commons is enabled by NCRIS.

    ARDC CONNECT NEWSLETTER

    Subscribe to the ARDC Connect Newsletter to keep up-to-date with the latest digital research news, events, resources, career opportunities and more.

    Subscribe

    Quick Links

    • Home
    • About Research Link Australia
    • Product Roadmap
    • Documentation
    • Disclaimer
    • Contact ARDC

    We acknowledge and celebrate the First Australians on whose traditional lands we live and work, and we pay our respects to Elders past, present and emerging.

    Copyright © ARDC. ACN 633 798 857 Terms and Conditions Privacy Policy Accessibility Statement
    Top
    Quick Feedback