Sparsity via Sparse Group k-max Regularization

For the linear inverse problem with sparsity constraints, the l_0 regularized problem is NP-hard, and existing approaches either utilize greedy algorithms to find almost-optimal solutions or to approximate the l_0 regularization with its convex counterparts. In this paper, we propose a novel and concise regularization, namely the sparse group k-max regularization, which can not only simultaneously enhance the group-wise and in-group sparsity, but also casts no additional restraints on the magnitude of variables in each group, which is especially important for variables at different scales, so that it approximate the l_0 norm more closely. We also establish an iterative soft thresholding algorithm with local optimality conditions and complexity analysis provided. Through numerical experiments on both synthetic and real-world datasets, we verify the effectiveness and flexibility of the proposed method.