On the Sequential Convergence of Lloyd’s Algorithms

Abstract

Lloyd’s algorithm is an iterative method that solves the quantization problem, that is, the approximation of a target probability measure by a discrete one, and is particularly used in digital applications. This algorithm can be interpreted as a gradient method on a certain quantization functional which is given by optimal transport. We study the sequential convergence (to a single accumulation point) for two variants of Lloyd’s method: (i) optimal quantization with an arbitrary discrete measure and (ii) uniform quantization with a uniform discrete measure. For both cases, we prove sequential convergence of the iterates under an analyticity assumption on the density of the target measure. This includes for example analytic densities truncated to a compact semialgebraic set. The argument leverages the log-analytic nature of globally subanalytic integrals, the interpretation of Lloyd’s method as a gradient method, and the convergence analysis of gradient algorithms under Kurdyka–Łojasiewicz assumptions. As a by-product, we also obtain definability results for more general semidiscrete optimal transport losses such as transport distances with general costs, the max-sliced Wasserstein distance, and the entropy regularized optimal transport loss.

Keywords

Reference

Léo Portales, Elsa Cazelles, and Edouard Pauwels, “On the Sequential Convergence of Lloyd’s Algorithms”, Mathematics of Operations Research, 2025, forthcoming.