3 papers
math.NA2026
Towards Universal Convergence of Backward Error in Linear System Solvers
Michał Dereziński, Yuji Nakatsukasa, Elizaveta Rebrova
The quest for an algorithm that solves an linear system in time complexity, or when solving up to relative error, is a long-stand…
math.NA2026
Adaptive LSQR Preconditioning from One Small Sketch
Jung Eun Huh, Coralia Cartis, Yuji Nakatsukasa
We propose APLICUR, an adaptive preconditioning framework for large-scale linear least-squares (LLS) problems. Using a single small sketch computed once at initialization, APLICUR…
math.NA2024
Eigen-componentwise convergence of SGD on quadratic programming
Lehan Chen, Yuji Nakatsukasa
Stochastic gradient descent (SGD) is a workhorse algorithm for solving large-scale optimization problems in data science and machine learning. Understanding the convergence of SGD…