Achieving acceleration in distributed optimization via direct discretization of the heavy-ball ODE
© 2019 American Automatic Control Council. We develop a distributed algorithm for convex Empirical Risk Minimization, the problem of minimizing large but finite sum of convex functions over networks. The proposed algorithm is derived from directly discretizing the second-order heavy-ball differentia...
Main Authors: | , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
IEEE
2023
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Online Access: | https://hdl.handle.net/1721.1/148596 |