3 papers
cs.LG2026
On the Width Scaling of Neural Optimizers Under Matrix Operator Norms I: Row/Column Normalization and Hyperparameter Transfer
Ruihan Xu, Jiajin Li, Yiping Lu
A central question in modern deep learning is how to design optimizers whose behavior remains stable as the network width increases. We address this question by interpreting se…
math.NA2025
What is a Sketch-and-Precondition Derivation for Low-Rank Approximation? Inverse Power Error or Inverse Power Estimation?
Ruihan Xu, Yiping Lu
Randomized sketching accelerates large-scale numerical linear algebra by reducing computational complexity. While the traditional sketch-and-solve approach reduces the problem size…
math.NA2024
Randomized Iterative Solver as Iterative Refinement: A Simple Fix Towards Backward Stability
Ruihan Xu, Yiping Lu
Iterative sketching and sketch-and-precondition are well-established randomized algorithms for solving large-scale, over-determined linear least-squares problems. In this paper, we…