Sharp convergence rate of the estimation error of LASSO via the Feature Space Decomposition method

arXiv:2610.30622v1 Announce Type: new Abstract: In this paper, we use the Feature Space Decomposition (FSD) method to establish an explicit, non-asymptotic, instance-optimal convergence rate for the estimation error of the LASSO in the Gaussian linear regression problem for any signal, any diagonal covariance and any regularization parameter above $\sigma_\xi/\sqrt{N}$ where $\sigma_\xi$ is the noise variance and $N$ the number of data.