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Convergence Rate of Incremental Gradient and Newton Methods
Mert Gürbüzbalaban, Asuman Ozdaglar, Pablo Parrilo
The incremental gradient method is a prominent algorithm for minimizing a finite sum of smooth convex functions, used in many contexts including large-scale data processing applica…
Why Random Reshuffling Beats Stochastic Gradient Descent
Mert Gürbüzbalaban, Asuman Ozdaglar, Pablo Parrilo
We analyze the convergence rate of the random reshuffling (RR) method, which is a randomized first-order incremental algorithm for minimizing a finite sum of convex component funct…
On the Convergence Rate of Incremental Aggregated Gradient Algorithms
Mert Gurbuzbalaban, Asuman Ozdaglar, Pablo Parrilo
Motivated by applications to distributed optimization over networks and large-scale data processing in machine learning, we analyze the deterministic incremental aggregated gradien…