Sketching Algorithms and Lower Bounds for Ridge Regression. (arXiv:2204.06653v1 [cs.DS])

We give a sketching-based iterative algorithm that computes $1+\varepsilon$
approximate solutions for the ridge regression problem $\min_x \|{Ax-b}\|_2^2
+\lambda\|{x}\|_2^2$ where $A \in \mathbb{R}^{n \times d}$ with $d \ge n$. Our
algorithm, for a constant number of iterations (requiring a constant number of
passes over the input), improves upon earlier work of Chowdhury et al., by
requiring that the sketching matrix only has a weaker Approximate Matrix
Multiplication (AMM) guarantee that depends on $\epsilon$, along with a
constant subspace embedding guarantee. …

algorithms arxiv regression ridge