- math
- optimization
- coding
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On the convergence of Gradient Descent
This blog will present the convergence rate of Gradient Descent (GD) algorithm with different assumptions on the objective function
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Some useful results on (strong) convexity and smoothness
Convexity and smoothness are two of the most used assumptions in the convergence analysis of optimization algorithm. In this post, I would like to present some useful results on these two important properties.