A primal 0/1-loss composite problem is recast as a nonconvex ℓ0-regularized dual, and a subspace gradient semismooth Newton method solves it with global and locally quadratic convergence.
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Composite Optimization with Indicator Functions: Stationary Duality and a Semismooth Newton Method
A primal 0/1-loss composite problem is recast as a nonconvex ℓ0-regularized dual, and a subspace gradient semismooth Newton method solves it with global and locally quadratic convergence.