A textbook property of optimization algorithms is their ability to optimize problems under generic regularity conditions. However, the performance of these fundamental and general-purpose optimization algorithms is often unsatisfactory; indeed, for many real problems, the gains from leveraging special structures can be huge.
A basic question then arises: how can we harness problem-specific structure within our algorithms to obtain fast, practical algorithms with strong performance guarantees? Although this line of research – which has been studied extensively for over 70 years – has enjoyed widespread success, the recent reliable and/or multi-agent machine-learning success stories have introduced new formulations ripe for deep theoretical analysis and remarkable practical impact.
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Website: | Visit Publisher Website |
Publisher: | University of California, Berkeley |
Published: | May 8, 2023 |
Copyright: | © 2023, by the author(s). All rights reserved. |