A relative inexact proximal ALM with a tailored semismooth Newton solver solves sparse spectral-risk optimization faster than ADMM while matching stationarity and sparsity on synthetic and real data.
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A Semismooth Newton Augmented Lagrangian Method for Sparse Spectral Risk Optimization
A relative inexact proximal ALM with a tailored semismooth Newton solver solves sparse spectral-risk optimization faster than ADMM while matching stationarity and sparsity on synthetic and real data.