Research map / Optimization / Nonconvex and smooth optimization
A Semismooth Newton Method for Fast, Generic Convex Programming
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply Newton-ADMM (semismooth Newton on SCS ADMM residuals) instead of plain SCS | SCS on generic cone programs · random LP, portfolio, logistic regression, robust PCA | wallclock time to optimal objective and solution | Helped |
| Apply Newton-ADMM instead of SCS on robust PCA | SCS on robust PCA (Kpsd, Kno cones) · robust PCA | wallclock time to optimal objective and solution | No clear effect |
| Use Newton method on ADMM residuals for lasso (specialized Newton-ADMM) instead of standard ADMM (ablation) | lasso · lasso p=400, N=200 | wallclock time to suboptimality | Helped |
| Newton-ADMM applied to sparse inverse covariance estimation vs QUIC and ADMM (ablation) | sparse inverse covariance estimation · p=N=1000 | wallclock time to suboptimality | Helped |
| Use exact Newton-ADMM with truncated GMRES tolerance schedule | Newton-ADMM · all experiments | quadratic local convergence | Helped |
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