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• W3012645498 abstract "The article discusses distributed gradient-descent algorithms for computing local and global minima in nonconvex optimization. For local optimization, we focus on distributed stochastic gradient descent (D-SGD)--a simple network-based variant of classical SGD. We discuss local minima convergence guarantees and explore the simple but critical role of the stable-manifold theorem in analyzing saddle-point avoidance. For global optimization, we discuss annealing-based methods in which slowly decaying noise is added to D-SGD. Conditions are discussed under which convergence to global minima is guaranteed. Numerical examples illustrate the key concepts in the paper." @default.
• W3012645498 created "2020-03-27" @default.
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• W3012645498 date "2020-03-23" @default.
• W3012645498 modified "2023-09-24" @default.
• W3012645498 title "Distributed Gradient Methods for Nonconvex Optimization: Local and Global Convergence Guarantees" @default.
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