This Project Grant award from the National Science Foundation (CFDA 47.049 - Mathematical and Physical Sciences) to Rensselaer Polytechnic Institute (RPI) provides $220,000 to develop new stochastic algorithms for solving minimax-structured nonconvex and nonsmooth optimization problems. The research aims to improve the robustness of deep learning models against adversarial attacks, with applications in areas like game theory, statistics, engineering, and machine learning. Key deliverables include new optimization algorithms, software packages for public use, and integration of the results into academic courses at RPI. The project will leverage problem structures and regularity conditions to design efficient primal-dual and smoothed algorithms, as well as distributed methods for large-scale data. The work is expected to lead to novel analysis techniques and effective algorithms for solving complex minimax optimization problems.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $220.0k | 6/30/25 |