Improved Monte Carlo Methods for Estimation, Optimisation, and Counting (2009–2013)

The current prevalence of Monte Carlo methods in science is a tribute to their usefulness for solving real-world problems. However, as the complexity of such problems continues to grow, classical methods increasingly run into problems in terms of speed and accuracy. The last few years have seen exciting new theoretical and conceptual developments in Monte Carlo techniques, providing dramatic improvements in performance. The project aims at maintaining the momentum of research and creating a theoretical breakthrough in the field. A spin-off will be the development of much-needed generic algorithms and software for solving complex estimation, optimisation, and counting problems over a wide range of applications.
Grant type:
ARC Discovery Projects
Funded by:
Australian Research Council