Computing Kurdyka-Łojasiewicz exponents via composition and symmetry

We devise calculus rules for the Kurdyka-Łojasiewicz exponent using the rank theorem and Lie group actions. They apply to a wide class of composite and invariant functions, and are particularly suitable for handling nonisolated local minima. Notably, smoothness plays no role, eschewing gradient and Hessian computations. This provides a unified framework for establishing linear convergence of various algorithms in matrix factorization, ${\mathrm{\ell }}_1$-matrix factorization, matrix sensing, and linear neural networks.