Studying Nesterov's Smoothing Technique
A brief summary of how smoothing bridges the gap between smooth and nonsmooth optimization.
Background and Description
Among first-order nonlinear optimization, there is a significant gap between the best methods for smooth and nonsmooth objective functions. Although these methods are "provably optimal", that does not necessarily mean that we cannot do any better. Nesterov's smoothing technique proposes the idea that rather than directly minimize a nonsmooth function, we can instead approximate it by a smooth one and minimize the approximation instead. This is a brief summary of the approach.
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See the derivation, examples, and the intuition behind smoothing.