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stat.ML2025
Inertial Quadratic Majorization Minimization with Application to Kernel Regularized Learning
Qiang Heng, Caixing Wang
First-order methods in convex optimization offer low per-iteration cost but often suffer from slow convergence, while second-order methods achieve fast local convergence at the exp…
stat.CO2025
Tactics for Improving Least Squares Estimation
Qiang Heng, Hua Zhou, Kenneth Lange
This paper deals with tactics for fast computation in least squares regression in high dimensions. These tactics include: (a) the majorization-minimization (MM) principle, (b) smoo…