A unified variance-reduced accelerated gradient method for convex optimization

NeurIPS 2019 Guanghui LanZhize LiYi Zhou

We propose a novel randomized incremental gradient algorithm, namely, VAriance-Reduced Accelerated Gradient (Varag), for finite-sum optimization. Equipped with a unified step-size policy that adjusts itself to the value of the condition number, Varag exhibits the unified optimal rates of convergence for solving smooth convex finite-sum problems directly regardless of their strong convexity... (read more)

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