Log-sum regularization with adaptive smoothing for the proximal operator yields state-evolution predictions that match AMP and ADMM performance, outperforming l1 regularization in low-density or high-measurement-rate regimes.
Asymptotic analysis of MAP estimation via the replica method and compressed sensing
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Sparse Signal Recovery using Log-Sum Regularization and Adaptive Smoothing
Log-sum regularization with adaptive smoothing for the proximal operator yields state-evolution predictions that match AMP and ADMM performance, outperforming l1 regularization in low-density or high-measurement-rate regimes.