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20162023
most citedAccelerated Methods for Non-Convex Optimization

53 citations · 56 across the 7 of their papers we have counts for

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math.OC2024

Closing the Computational-Query Depth Gap in Parallel Stochastic Convex Optimization

Arun Jambulapati, Aaron Sidford, Kevin Tian

We develop a new parallel algorithm for minimizing Lipschitz, convex functions with a stochastic subgradient oracle. The total number of queries made and the query depth, i.e., the…

math.OC2016★ 53 cited

Accelerated Methods for Non-Convex Optimization

Yair Carmon, John C. Duchi, Oliver Hinder +1

We present an accelerated gradient method for non-convex optimization problems with Lipschitz continuous first and second derivatives. The method requires time $O(ε^{-7/4} \log(1/…