A Proximal-Gradient Homotopy Method for the Sparse Least-Squares Problem
This work addresses computational efficiency in sparse recovery applications like compressed sensing, offering a theoretical improvement over existing methods.
The paper tackles the slow convergence of proximal gradient methods for sparse least-squares problems by proposing a homotopy continuation strategy that exploits local linear convergence, achieving a global geometric convergence rate with an iteration complexity of O(log(1/ε)).
We consider solving the $\ell_1$-regularized least-squares ($\ell_1$-LS) problem in the context of sparse recovery, for applications such as compressed sensing. The standard proximal gradient method, also known as iterative soft-thresholding when applied to this problem, has low computational cost per iteration but a rather slow convergence rate. Nevertheless, when the solution is sparse, it often exhibits fast linear convergence in the final stage. We exploit the local linear convergence using a homotopy continuation strategy, i.e., we solve the $\ell_1$-LS problem for a sequence of decreasing values of the regularization parameter, and use an approximate solution at the end of each stage to warm start the next stage. Although similar strategies have been studied in the literature, there have been no theoretical analysis of their global iteration complexity. This paper shows that under suitable assumptions for sparse recovery, the proposed homotopy strategy ensures that all iterates along the homotopy solution path are sparse. Therefore the objective function is effectively strongly convex along the solution path, and geometric convergence at each stage can be established. As a result, the overall iteration complexity of our method is $O(\log(1/ε))$ for finding an $ε$-optimal solution, which can be interpreted as global geometric rate of convergence. We also present empirical results to support our theoretical analysis.