The Power of Normalization: Faster Evasion of Saddle Points

15 Nov 2016Kfir Y. Levy

A commonly used heuristic in non-convex optimization is Normalized Gradient Descent (NGD) - a variant of gradient descent in which only the direction of the gradient is taken into account and its magnitude ignored. We analyze this heuristic and show that with carefully chosen parameters and noise injection, this method can provably evade saddle points... (read more)

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