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arXiv · 2110.15470

New insights in smoothness and strong convexity with improved convergence of gradient descent

Lu Zhang·Jiani Wang·Hui Zhang
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Abstract

The starting assumptions to study the convergence and complexity of gradient-type methods may be the smoothness (also called Lipschitz continuity of gradient) and the strong convexity. In this note, we revisit these two basic properties from a new perspective that motivates their definitions and equivalent characterizations, along with an improved linear convergence of the gradient descent method.

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Lu Zhang, Jiani Wang, Hui Zhang. 2021-10-29. New insights in smoothness and strong convexity with improved convergence of gradient descent. https://arxiv.org/abs/2110.15470

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