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|Abstract:||We design a non-convex second-order optimization algorithm that is guaranteed to return an approximate local minimum in time which scales linearly in the underlying dimension and the number of training examples. The time complexity of our algorithm to find an approximate local minimum is even faster than that of gradient descent to find a critical point. Our algorithm applies to a general class of optimization problems including training a neural network and other non-convex objectives arising in machine learning.|
|Citation:||Agarwal, Naman, Zeyuan Allen-Zhu, Brian Bullins, Elad Hazan, and Tengyu Ma. "Finding approximate local minima faster than gradient descent." In Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing (2017): pp. 1195-1199. doi:10.1145/3055399.3055464|
|Pages:||1195 - 1199|
|Type of Material:||Conference Article|
|Journal/Proceeding Title:||Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing|
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