CAREER: Foundations and Algorithms for Nonconvex Nonsmooth Optimization: From Local Solver — NSF Award to University of California
Optimization serves as the mathematical engine powering modern artificial intelligence and complex decision-making systems. Many real-world challenges, however, ranging from managing energy grids to training machine learning models, involve mathematical landscapes that are jagged, unpredictable, and obscured by data no
| Award title | CAREER: Foundations and Algorithms for Nonconvex Nonsmooth Optimization: From Local Solver |
|---|---|
| Award ID | 2541022 |
| Awardee | University of California-Berkeley |
| City | BERKELEY |
| State | CA |
| Amount obligated | $364,815 |
| Principal investigator | Ying Cui |
| Program | Comm & Information Foundations |
| Start date | 04/15/2026 |
| Abstract | Optimization serves as the mathematical engine powering modern artificial intelligence and complex decision-making systems. Many real-world challenges, however, ranging from managing energy grids to training machine learning models, involve mathematical landscapes that are jagged, unpredictable, and obscured by data noise. These irregularities often trap existing technologies in suboptimal or inefficient solutions. This project pursues a new generation of rigorous mathematical tools and stable a |
| Source | NSF Awards |
$799/mo
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