# Convergence of first order subgradient methods for non-convex functions

This lecture note gives the proof for how deterministic subgradient method converges on non-smooth convex and strongly convex Lipschitz functions.

Is there a non-convex version of this proof?

Like convergence of subgradient method for,

• non-smooth Lipschitz non-convex functions (like $$\frac{1}{1+e^{-\vert x \vert}}$$) ?
• non-smooth non-Lipschitz non-convex functions (like $$(1-\vert x \vert)^2$$)?

(happy to get references for the the stochastic versions of these too!)

• On non-smooth optimization, better ask at Math SE, I would guess. There's a tag for it there. Mar 23, 2021 at 4:35
• Okay. If I dont get answers in a couple of days, I will move it there :D Mar 23, 2021 at 4:54