Consistent Parameter Estimation for LASSO and Approximate Message Passing

We consider the problem of recovering a vector βₒ Rᵖ from n random and noisy linear observations y= Xβₒ + w, where X is the measurement matrix and w is noise. The LASSO estimate is given by the solution to the optimization problem β_λ = _β 12 \|y-Xβ\|₂² + λ\| β\|₁. Among the iterative algorithms that have been proposed for solving this optimization problem, approximate message passing (AMP) has attracted attention for its fast convergence. Despite significant progress in the theoretical analysis of the estimates of LASSO and AMP, little is known about their behavior as a function of the regularization parameter λ, or the thereshold parameters τᵗ. For instance the following basic questions have not yet been studied in the literature: (i) How does the size of the active set \|β^λ\|₀/p behave as a function of λ? (ii) How does the mean square error \|β_λ - βₒ\|₂²/p behave as a function of λ? (iii) How does \|βᵗ - βₒ \|₂²/p behave as a function of τ¹, , τ^t-1? Answering these questions will help in addressing practical challenges regarding the optimal tuning of λ or τ¹, τ²,. This paper answers these questions in the asymptotic setting and shows how these results can be employed in deriving simple and theoretically optimal approaches for tuning the parameters τ¹, , τᵗ for AMP or λ for LASSO. It also explores the connection between the optimal tuning of the parameters of AMP and the optimal tuning of LASSO.