The K-means algorithm is one of the most widely studied clustering algorithms in machine learning. While extensive research has focused on its ability to achieve a globally optimal solution, there still lacks a rigorous analysis of its local optimality guarantees. In this paper, we first present conditions under which the K-means algorithm converges to a locally optimal solution. Based on this, we propose simple modifications to the K-means algorithm which ensure local optimality in both the continuous and discrete sense, with the same computational complexity as the original K-means algorithm. As the dissimilarity measure, we consider a general Bregman divergence, which is an extension of the squared Euclidean distance often used in the K-means algorithm. Numerical experiments confirm that the K-means algorithm does not always find a locally optimal solution in practice, while our proposed methods provide improved locally optimal solutions with reduced clustering loss. Our code is available at https://github.com/lmingyi/LO-K-means.

This paper is motivated by structured sparsity for deep neural network training. We study a weighted group $l_0$-norm constraint, and present the projection and normal cone of this set. Using randomized smoothing, we develop zeroth and first-order algorithms for minimizing a Lipschitz continuous function constrained by any closed set which can be projected onto. Non-asymptotic convergence guarantees are proven in expectation for the proposed algorithms for two related convergence criteria which can be considered as approximate stationary points. Two further methods are given using the proposed algorithms: one with non-asymptotic convergence guarantees in high probability, and the other with asymptotic guarantees to a stationary point almost surely. We believe in particular that these are the first such non-asymptotic convergence results for constrained Lipschitz continuous loss functions.

This paper focuses on stochastic proximal gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer and convex constraints. To the best of our knowledge we present the first non-asymptotic convergence bounds for this class of problem. We present two simple stochastic proximal gradient algorithms, for general stochastic and finite-sum optimization problems. In a numerical experiment we compare our algorithms with the current state-of-the-art deterministic algorithm and find our algorithms to exhibit superior convergence.

Our work focuses on stochastic gradient methods for optimizing a smooth non-convex loss function with a non-smooth non-convex regularizer. Research on this class of problem is quite limited, and until recently no non-asymptotic convergence results have been reported. We present two simple stochastic gradient algorithms, for finite-sum and general stochastic optimization problems, which have superior convergence complexities compared to the current state-of-the-art. We also compare our algorithms’ performance in practice for empirical risk minimization.