Preprint
Jul 2026
A Semismooth Newton Augmented Lagrangian Method for Sparse Spectral Risk Optimization
This work proposes a relative inexact proximal augmented Lagrangian method with a semismooth Newton subproblem solver for solving SRM-based optimization problems and provides explicit generalized Jacobian characterizations and tailor the pool adjacent violators algorithm for their efficient evaluation.
Rufeng Xiao, Rujun Jiang, Xudong Li et al.
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