Collaborative Research: Adaptive Gaussian Markov Random Fields for Large-scale Discrete Optimization via Simulation

The investigators propose to create theory and algorithms to solve large-scale constrained DOvS problems that deliver strong optimality-gap inference at termination, and that exploit efficient computational linear algebra tailored to high-performance computing environments. Our methods are specifically targeted to go beyond the range of current rigorously justified methods.