Real-time scheduling of trains to control peak electricity demand. This project aims to develop new scheduling and control methods that will enable railways to reduce their demand for electricity during peak demand periods, without undue disruption to the timetable.
These new methods and systems will integrate with—and expand the capabilities of—an Australian train control system that is used by railways around the world. This will enable better management of electricity within a region and be ....Real-time scheduling of trains to control peak electricity demand. This project aims to develop new scheduling and control methods that will enable railways to reduce their demand for electricity during peak demand periods, without undue disruption to the timetable.
These new methods and systems will integrate with—and expand the capabilities of—an Australian train control system that is used by railways around the world. This will enable better management of electricity within a region and better use of renewable energy sources, with significant cost savings for railways and the wider community.Read moreRead less
Geometry in projection methods and fixed-point theory. This project aims to resolve mathematical challenges arising from problems with specific structure typical for key modern applications, such as big data optimisation, chemical engineering and medical imaging. We focus on developing new mathematical tools for the analysis of projection methods and accompanying fixed point theory, specifically targeting the refinement of the geometric intuition for algorithm design techniques to inform the imp ....Geometry in projection methods and fixed-point theory. This project aims to resolve mathematical challenges arising from problems with specific structure typical for key modern applications, such as big data optimisation, chemical engineering and medical imaging. We focus on developing new mathematical tools for the analysis of projection methods and accompanying fixed point theory, specifically targeting the refinement of the geometric intuition for algorithm design techniques to inform the implementation of optimal methods for huge-scale optimisation problems.Read moreRead less
An optimisation-based framework for non-classical Chebyshev approximation. This project aims to solve open mathematical problems in multivariate and piecewise polynomial approximations, two directions that correspond to fundamental obstacles to extending classical approximation results. Through an innovative combination of optimisation and algebraic technique, the project intends to develop foundations for new results in approximation theory, and new insights into other areas of mathematics, mos ....An optimisation-based framework for non-classical Chebyshev approximation. This project aims to solve open mathematical problems in multivariate and piecewise polynomial approximations, two directions that correspond to fundamental obstacles to extending classical approximation results. Through an innovative combination of optimisation and algebraic technique, the project intends to develop foundations for new results in approximation theory, and new insights into other areas of mathematics, most notably optimisation. The techniques and methods developed should also have significant benefits in the many disciplines where approximation problems appear, such as engineering, physics or data mining. The research outputs resulting from this project will be used in a wide range of fields to help implement programs, policies and improve decision making.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100006
Funder
Australian Research Council
Funding Amount
$444,847.00
Summary
Robust Derivative-Free Algorithms for Complex Optimisation Problems. Mathematical optimisation gives a systematic way for optimal decision-making. This project aims to develop new mathematical tools for complex optimisation problems where limited problem information is available. It will generate new foundational theories for alternative optimisation tools, introducing substantial new capability and rigour to the discipline. The project will create significant new mathematical optimisation techn ....Robust Derivative-Free Algorithms for Complex Optimisation Problems. Mathematical optimisation gives a systematic way for optimal decision-making. This project aims to develop new mathematical tools for complex optimisation problems where limited problem information is available. It will generate new foundational theories for alternative optimisation tools, introducing substantial new capability and rigour to the discipline. The project will create significant new mathematical optimisation techniques and create world-leading and publicly available software. These new techniques and software may ultimately be able to solve some of the most complex optimisation problems in research and industry, such as improving long-term climate predictions and designing 3D-printed medical implants.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC200100009
Funder
Australian Research Council
Funding Amount
$4,861,236.00
Summary
ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA). OPTIMA addresses industry’s urgent need for decision-making tools for global competitiveness: reducing lead times, and financial and environmental costs, while improving efficiency, quality, and agility. Despite strong expertise in academia, industry is yet to fully benefit from optimisation technology due to its high barrier to entry. Connecting industry partners with world-leading interdiscip ....ARC Training Centre in Optimisation Technologies, Integrated Methodologies, and Applications (OPTIMA). OPTIMA addresses industry’s urgent need for decision-making tools for global competitiveness: reducing lead times, and financial and environmental costs, while improving efficiency, quality, and agility. Despite strong expertise in academia, industry is yet to fully benefit from optimisation technology due to its high barrier to entry. Connecting industry partners with world-leading interdisciplinary researchers and talented students, OPTIMA will advance an industry-ready optimisation toolkit, while training a new generation of industry practitioners and over 120 young researchers, vanguarding a highly skilled workforce of change agents for transformation of the advanced manufacturing, energy resources, and critical infrastructure sectors.Read moreRead less
Relaxed reflection methods for feasibility and matrix completion problems. The project proposes to further develop the non-linear convergence theory, and to provide problem-specific implementations. Many applied and pure problems require solution of a large set of linear or nonlinear equations (or inequalities). Highly effective, parallelisable methods are based on iterated projection or reflection algorithms which aggregate information about individual equations. The theory is well developed in ....Relaxed reflection methods for feasibility and matrix completion problems. The project proposes to further develop the non-linear convergence theory, and to provide problem-specific implementations. Many applied and pure problems require solution of a large set of linear or nonlinear equations (or inequalities). Highly effective, parallelisable methods are based on iterated projection or reflection algorithms which aggregate information about individual equations. The theory is well developed in the linear case, but does not explain many important applications for which they are often highly successful (eg optical aberration correction, protein reconstruction, tomography, compressed sensing). The project also plans to provide heuristics to help explain why an algorithm performs well on one class of applications but fails on another.Read moreRead less
Innovations in sparse optimisation: big data nonsmooth optimisation. This project aims to produce innovative optimisation methods capable of solving a wide range of practical problems that are currently too complex to be solved. Optimisation involving huge data sets is ubiquitous. Sparse optimisation has emerged as a challenging frontier of modern optimisation because it effectively computes an optimal solution with desired low complexity structure so that a resulting solution can be efficiently ....Innovations in sparse optimisation: big data nonsmooth optimisation. This project aims to produce innovative optimisation methods capable of solving a wide range of practical problems that are currently too complex to be solved. Optimisation involving huge data sets is ubiquitous. Sparse optimisation has emerged as a challenging frontier of modern optimisation because it effectively computes an optimal solution with desired low complexity structure so that a resulting solution can be efficiently stored, implemented and utilised, and is robust to the data inexactness. This project aims at developing innovative mathematical techniques and efficient numerical schemes for solving sparse optimisation problems. The intended outcomes will have significant impact on many areas of science, medicine and engineering, where sparse optimisation is used, including cancer radiotherapy optimal planning.Read moreRead less
Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation ....Big temporal graph processing in the Cloud. This project aims to develop efficient and scalable algorithms to process big temporal graphs in the Cloud. In particular, we will investigate three most representative types of queries over big temporal graphs including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240100668
Funder
Australian Research Council
Funding Amount
$435,000.00
Summary
Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations ....Towards Processing of Big Streaming Temporal Graphs. This project aims to develop efficient and scalable algorithms to process big streaming temporal graphs, which is in high demand for many data-intensive applications such as cybersecurity, crime monitoring, and e-marketing. In particular, I will investigate three most representative types of queries including vertex-based queries, path-based queries, and subgraph-based queries. Expected outcomes of this project include theoretical foundations and scalable algorithms to process big streaming temporal graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and social analysis.Read moreRead less
Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engin ....Next-Generation Distributed Graph Engine for Big Graphs. This project aims to develop an efficient and scalable distributed graph engine to process big graphs. In particular, we will investigate the foundations for the distributed real-time graph engine, focusing on graph storage and graph operators, and then provide solutions for a set of representative graph mining and query processing tasks. Expected outcomes of this project include theoretical foundations and a scalable real-time graph engine to process big graphs as well as a system prototype for evaluation and to demonstrate the practical value. Success in this project should see significant benefits for many important applications such as cybersecurity, e-commerce, health and road networks.Read moreRead less