Relative Weak Convexity and Projected Subgradient Methods: Analysis and Convergence

Abstract

We introduce the class of relatively weakly convex functions, which extends the classical notion of weak convexity by measuring nonconvexity relative to a distance-generating function. We investigate the fundamental properties of this function class, establishing characterization results, calculus rules, and illustrative examples. We further analyze the associated optimization landscape and identify a neighborhood of the set of global minimizers that is free of saddle points. Motivated by this geometric structure, we propose the Projected SubGradient Algorithm (PSGA) with several step-size strategies. Under a sharpness error bound, we prove that, when initialized within this saddle-point-free neighborhood, the iterates generated by PSGA converge to a global minimizer for each of the proposed step-size strategies. Furthermore, linear convergence is established for the geometrically decaying step-size strategy.