Proximal gradient method
Proximal gradient methods are a generalized form of projection used to solve non-differentiable convex optimization problems. Many interesting problems can be formulated as convex optimization problems of the form min x ∈ R d ∑ i = 1 n f i ( x ) {\displaystyle \min _{\mathbf {x} \in \mathbb {R} ^{d}}\sum _{i=1}^{n}f_{i}(\mathbf {x} )} where f i : R d → R , i = 1 , … , n {\displaystyle f_{i}:\mathbb {R} ^{d}\rightarrow \mathbb {R} ,\ i=1,\dots ,n} are possibly non-differentiable convex functions.