Unlock: Subgradients and Subdifferentials
The non-smooth generalization of the gradient for convex functions. Subgradients enable optimality conditions, calculus rules, and convergence guarantees for L1-regularized problems, hinge loss SVMs, and proximal algorithms where the objective is not differentiable.
32 Prerequisites0 Mastered0 Working30 Gaps
Prerequisite mastery6%
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