Achieving acceleration in distributed optimization via direct discretization of the heavy-ball ODE
Name
distributed_ode_discretization_final.pdf
Description
Accepted version
Size
405.33 KB
Format
Adobe PDF
Checksum (MD5)
9dd8f75c5dc30bbb6883776588b8ec2a
Author(s)
Zhang, J
Uribe, CA
Mokhtari, A
Jadbabaie, A
Date Issued
July 1, 2019
Journal
Proceedings of the American Control Conference
Publisher
IEEE
Citation
Zhang, J, Uribe, CA, Mokhtari, A and Jadbabaie, A. 2019. "Achieving acceleration in distributed optimization via direct discretization of the heavy-ball ODE." Proceedings of the American Control Conference, 2019-July.
Version
Author's final manuscript
Abstract
© 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 differential equation and results in an accelerated convergence rate, i.e., faster than distributed gradient descent-based methods for strongly convex objectives that may not be smooth. Notably, we achieve acceleration without resorting to the well-known Nesterov's momentum approach. We provide numerical experiments and contrast the proposed method with recently proposed optimal distributed optimization algorithms.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.23919/acc.2019.8814686
