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

Project: Research project

Project Details


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.
Effective start/end date7/15/196/30/23


  • National Science Foundation (DMS-1854562)


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