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math.OC2023
Variance Reduced Distributed Non-Convex Optimization Using Matrix Stepsizes
Hanmin Li, Avetik Karagulyan, Peter Richtárik
Matrix-stepsized gradient descent algorithms have been shown to have superior performance in non-convex optimization problems compared to their scalar counterparts. The det-CGD alg…
math.OC2023
Det-CGD: Compressed Gradient Descent with Matrix Stepsizes for Non-Convex Optimization
Hanmin Li, Avetik Karagulyan, Peter Richtárik
This paper introduces a new method for minimizing matrix-smooth non-convex objectives through the use of novel Compressed Gradient Descent (CGD) algorithms enhanced with a matrix-v…