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: | , , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023
|
| Online Access: | https://hdl.handle.net/1721.1/148596 |