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math.OC2022
SketchySGD: Reliable Stochastic Optimization via Randomized Curvature Estimates
Zachary Frangella, Pratik Rathore, Shipu Zhao +1
SketchySGD improves upon existing stochastic gradient methods in machine learning by using randomized low-rank approximations to the subsampled Hessian and by introducing an automa…
math.OC2022
NysADMM: faster composite convex optimization via low-rank approximation
Shipu Zhao, Zachary Frangella, Madeleine Udell
This paper develops a scalable new algorithm, called NysADMM, to minimize a smooth convex loss function with a convex regularizer. NysADMM accelerates the inexact Alternating Direc…