This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).
Objective
The objective of this program is to provide new breakthroughs in the areas of stochastic control and global optimization through insights gained from particle filtering and from additional recent results in nonlinear filtering. With a focus on applying the particle filtering methodology, the proposed research will result in (i) new computationally efficient algorithms for continuous-state partially observable Markov decision processes and global optimization, and (ii) rigorous analysis of the algorithms through the development of bounds and convergence proofs. In particular, for global optimization problems, the particle filtering framework can prove transformative by providing a firm analytical basis for understanding why algorithms work well, when algorithms break down, how to compare algorithms, which algorithm works better than the others for a specific problem, and how to develop new algorithms that should work well for particular problems.
Intellectual merit
Partially observable stochastic control and global optimization are areas with many theoretical challenges and many potential applications. To attack difficult problems of a size that are found in most applications will require significant new methodologies. The proposed approach based on particle filtering will provide new algorithms and rigorous analytical justification beyond that available with other methods.
Broader impacts
Stochastic control and optimization can be applied to many problems of critical concern in US industry, so the resulting algorithms will have broad and transformative applicability. In the project, they will be tested on problems in industries from telecommunications to manufacturing to finance. The project will closely integrate the training of PhD students.
