  We introduce a geometrically transparent strict saddle property for nonsmooth
functions. This property guarantees that simple proximal algorithms on weakly
convex problems converge only to local minimizers, when randomly initialized.
We argue that the strict saddle property may be a realistic assumption in
applications, since it provably holds for generic semi-algebraic optimization
problems.
