Collaborative Research: Gradient-free optimization of matrix functions — NSF Award to University of Colorado at Boulder (CO, $150,
Artificial intelligence models typically reduce to optimization problems: find the best solution according to a problem-specific metric. Sometimes, the nature of the problems means that the standard calculus-based tools cannot be applied. This setting is known as gradient-free optimization, and is particularly relevant
| Award title | Collaborative Research: Gradient-free optimization of matrix functions |
|---|---|
| Award ID | 2608660 |
| Awardee | University of Colorado at Boulder |
| City | BOULDER |
| State | CO |
| Amount obligated | $150,000 |
| Principal investigator | Stephen Becker |
| Program | COMPUTATIONAL MATHEMATICS |
| Start date | 07/01/2026 |
| Abstract | Artificial intelligence models typically reduce to optimization problems: find the best solution according to a problem-specific metric. Sometimes, the nature of the problems means that the standard calculus-based tools cannot be applied. This setting is known as gradient-free optimization, and is particularly relevant for small businesses, academic research groups, and public-sector organizations that lack large-scale computing infrastructure yet still need to fine-tune machine learning models |
| Source | NSF Awards |
$799/mo
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