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Second-Order Guarantees in Centralized, Federated and Decentralized Nonconvex Optimization
[article]
2020
arXiv
pre-print
Rapid advances in data collection and processing capabilities have allowed for the use of increasingly complex models that give rise to nonconvex optimization problems. These formulations, however, can be arbitrarily difficult to solve in general, in the sense that even simply verifying that a given point is a local minimum can be NP-hard [1]. Still, some relatively simple algorithms have been shown to lead to surprisingly good empirical results in many contexts of interest. Perhaps the most
arXiv:2003.14366v1
fatcat:42vsyhewprcaln2j7365ehb4zi