Title: Variance-Reduced Stochastic Learning under Random Reshuffling,
Abstract: Several useful variance-reduced stochastic gradient algorithms, such as SVRG,
SAGA, Finito, and SAG, have been proposed to minimize empirical risks with
linear convergence properties to the exact minimizer. The existing convergence
results assume uniform data sampling with replacement. However, it has been
observed in related works that random reshuffling can deliver superior
performance over uniform sampling and, yet, no formal proofs or guarantees of
exact convergence exist for variance-reduced algorithms under random
reshuffling. This paper makes two contributions. First, it resolves this open
issue and provides the first theoretical guarantee of linear convergence under
random reshuffling for SAGA; the argument is also adaptable to other
variance-reduced algorithms. Second, under random reshuffling, the paper
proposes a new amortized variance-reduced gradient (AVRG) algorithm with
constant storage requirements compared to SAGA and with balanced gradient
computations compared to SVRG. AVRG is also shown analytically to converge
linearly.