AI RESEARCH
Sequential Least-Squares Estimators with Fast Randomized Sketching for Linear Statistical Models
arXiv CS.LG
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ArXi:2509.06856v2 Announce Type: replace-cross We propose a novel randomized framework for the estimation problem of large-scale linear statistical models, namely Sequential Least-Squares Estimators with Fast Randomized Sketching (SLSE-FRS), which integrates Sketch-and-Solve and Iterative-Sketching methods for the first time. By iteratively constructing and solving sketched least-squares (LS) subproblems with increasing sketch sizes to achieve better precisions, SLSE-FRS gradually refines the estimators of the true parameter vector, ultimately producing high-precision estimators.