Final Performance Report on Grant FA9550-07-1-0366 (Simulation-Based and Sampling Method for Global Optimization)

摘要

The researchers made significant progress in all of the proposed research areas. The first major task in the proposal involved simulation-based and sampling methods for global optimization. In support of this task, we have discovered two new innovative approaches to simulation-based global optimization; the first involves connections between stochastic approximation and our model reference approach to global optimization, while the second connects particle filtering and simulation-based approaches to global optimization. We have also made significant progress in population-based global optimal search methods, applications of these algorithms to problems in statistics and clinical trials, and efficient allocation of simulations. In support of the second task, we have made progress incorporating simulation- based and sampling methods into Markov Decision Processes (MDPs). We have made significant progress on new sampling methods for MDPs, simulation-based approaches to partially observable Markov decision processes (POMDPs), and applications of these algorithms.