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.
Compressed sensing
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DU-PSISTA combines linear sketching with periodic ISTA and deep unfolding to achieve linear convergence to a neighborhood of the true sparse signal at lower computational cost when the period and sketch size are chosen appropriately.
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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.
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Computationally Efficient Sparse Signal Recovery via Linear Sketching and Deep Unfolding
DU-PSISTA combines linear sketching with periodic ISTA and deep unfolding to achieve linear convergence to a neighborhood of the true sparse signal at lower computational cost when the period and sketch size are chosen appropriately.
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