Aaron Jaech

Distributed Gradient Methods for Nonconvex Optimization: Local and\n Global Convergence Guarantees

2020 · arXiv (Cornell University)

The article discusses distributed gradient-descent algorithms for computing\nlocal and global minima in nonconvex optimization. For local optimization, we\nfocus on distributed stochastic gradient descent (D-SGD)--a simple\nnetwork-based variant of classical SGD. We discuss local minima convergence\nguarantees and …