CAREER: Efficient Algorithms for Generalized Quasi-Variational Inequalities in Stochastic — NSF Award to University of Arizona (AZ
This NSF CAREER project aims to develop foundational mathematical tools to address emerging challenges in distributed and uncertain systems, such as those in energy infrastructure, machine learning, and wireless communication. Despite recent advances in networked systems, current models and algorithms lack provable per
| Award title | CAREER: Efficient Algorithms for Generalized Quasi-Variational Inequalities in Stochastic |
|---|---|
| Award ID | 2439971 |
| Awardee | University of Arizona |
| City | TUCSON |
| State | AZ |
| Amount obligated | $512,830 |
| Principal investigator | Afrooz Jalilzadeh |
| Program | EPCL: Energy, Power, Control, |
| Start date | 10/01/2025 |
| Abstract | This NSF CAREER project aims to develop foundational mathematical tools to address emerging challenges in distributed and uncertain systems, such as those in energy infrastructure, machine learning, and wireless communication. Despite recent advances in networked systems, current models and algorithms lack provable performance guarantees for a broad class of critical problems. These include (i) Generalized Nash games, where agents compete over shared resources; (ii) Bilevel optimization with con |
| Source | NSF Awards |
$799/mo
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