An operator-splitting algorithm for the hypergraph $p$-Laplacian with applications to missing data recovery

Hypergraph $p$-Laplacian regularization is a fundamental model in data analysis with successful applications in various tasks.

It aims to minimize a nonsmooth and typically large-scale objective function defined as the sum of the $p$-th powers of the Lipschitz regularization over hyperedges.

In this paper, we propose an operator-splitting algorithm for the hypergraph $p$-Laplacian that allows us to handle hyperedges separately in a Gauss-Seidel fashion.

Each subproblem can be viewed as a generalized graph Lipschitz learning on a hyperedge, for which we introduce an auxiliary variable to overcome the nonsmoothness and solve it with one step of the alternating direction method of multipliers (ADMM).

The resulting algorithm performs proximal ADMM updates sequentially over the hyperedges, and its convergence is proven.

We test the algorithm on missing data recovery problems, including image sparse inpainting and semi-supervised learning, to demonstrate that it is faster than existing methods.