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cs.LG2024
Streamlining in the Riemannian Realm: Efficient Riemannian Optimization with Loopless Variance Reduction
Yury Demidovich, Grigory Malinovsky, Peter Richtárik
In this study, we investigate stochastic optimization on Riemannian manifolds, focusing on the crucial variance reduction mechanism used in both Euclidean and Riemannian settings.…
cs.LG2024
Correlated Quantization for Faster Nonconvex Distributed Optimization
Andrei Panferov, Yury Demidovich, Ahmad Rammal +1
Quantization (Alistarh et al., 2017) is an important (stochastic) compression technique that reduces the volume of transmitted bits during each communication round in distributed m…