3 papers
math.OC2026
On the Iterate Convergence of AdaGrad for Generalized Smooth Convex Optimization
Mathieu Besançon, Tung Quoc Le
We prove sequential convergence results for the AdaGrad algorithm family optimizing convex differentiable objectives. Specifically, we provide necessary and sufficient conditions f…
stat.ML2026
Provably Data-driven Lagrangian Relaxation for Mixed Integer Linear Programming
Tung Quoc Le, Anh Tuan Nguyen, Viet Anh Nguyen
Lagrangian Relaxation (LR) is a powerful technique for solving large-scale Mixed Integer Linear Programming (MILP), particularly those with decomposable structures, such as vehicle…
stat.ML2026
Provably Data-driven Multiple Hyper-parameter Tuning with Structured Loss Function
Tung Quoc Le, Anh Tuan Nguyen, Viet Anh Nguyen
Data-driven algorithm design automates hyperparameter tuning, but its statistical foundations remain limited because model performance can depend on hyperparameters in implicit and…