Projection onto the probability simplex: An efficient algorithm with a simple proof, and an application

This addresses a computational bottleneck in optimization and machine learning, particularly for clustering tasks, but is incremental as it builds on existing projection methods.

The paper tackles the problem of efficiently computing the Euclidean projection onto the probability simplex, presenting an algorithm with a simple proof, and applies it to Laplacian K-modes clustering.