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.
WEEP: A differen- tiable nonconvex sparse regularizer via weakly-convex envelope
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.IT 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.