Stationary duality reduces composite cardinality optimization to simple cardinality, yielding dual problems with equivalent local solutions and global solutions under appropriate parameter selection.
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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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On the Stationary Duality of Structural Composite Cardinality Optimization
Stationary duality reduces composite cardinality optimization to simple cardinality, yielding dual problems with equivalent local solutions and global solutions under appropriate parameter selection.
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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.