Fast dual proximal gradient algorithms with rate $O(1/k^{1.5})$ for convex minimization

We consider minimizing the composite function that consists of a strongly convex function and a convex function. The fast dual proximal gradient (FDPG) method decreases the dual function with a rate $O (1 / k^{2})$ , leading to a rate $O (1 / k)$ for decreasing the primal function. We propose a generalized FDPG method that guarantees an $O (1 / k^{1.5})$ rate for the dual proximal gradient norm decrease. By relating this to the primal function decrease, the proposed approach decreases the primal function with the improved $O (1 / k^{1.5})$ rate.

Fast dual proximal gradient algorithms with rate $O (1 / k^{1.5})$ for convex minimization

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Fast dual proximal gradient algorithms with rate $O (1 / k^{1.5})$ for convex minimization

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Fast dual proximal gradient algorithms with rate O(1/k1.5)O(1/k1.5) for convex minimization

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Fast dual proximal gradient algorithms with rate O(1/k1.5)O(1/k1.5) for convex minimization

文献摘要

讨论

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Fast dual proximal gradient algorithms with rate $O (1 / k^{1.5})$ for convex minimization

讲解博客

AI智能校验

Fast dual proximal gradient algorithms with rate $O (1 / k^{1.5})$ for convex minimization

讲解博客

AI智能校验