Proximal random reshuffling under local Lipschitz continuity

Understand

We study proximal random reshuffling (PRR) for minimizing the sum of locally Lipschitz or locally smooth functions and a proper lower semicontinuous convex function without assuming coercivity or the existence of limit points.

• The algorithmic guarantees pertaining to near approximate stationarity rely on a new tracking lemma linking the iterates to trajectories of conservative fields.
• One of the novelties in the analysis consists in handling set-valued mappings with unbounded values.
• In the locally smooth case, it improves the known convergence rate from nearly $O(k^{-1/4})$ to nearly $o(k^{-1/2})$.