This Project Grant award from the National Science Foundation (NSF) Computer and Information Science and Engineering (CISE) program, CFDA 47.070, provides $525,000 to The Johns Hopkins University to conduct collaborative research on innovative theories and algorithms for min-max optimization. The research aims to develop specialized theoretical frameworks and efficient algorithms tailored explicitly to min-max optimization, which underpins technologies ranging from generative AI to large-scale reinforcement learning. The project will explore accelerated convergence through anchor-type algorithms, enhanced stochastic methods, and practical algorithms robust to non-ideal conditions, including nonconvex problems and efficient sampling strategies. This research is expected to significantly enhance the efficiency and robustness of min-max optimization, with direct impacts on practical applications in machine learning and artificial intelligence. The award has an ultimate completion date of July 31, 2028.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $525.0k | 7/8/25 |