Randomized Kaczmarz converges along small singular vectors
Not on the map. Why?
Abstract (arXiv)
Randomized Kaczmarz is a simple iterative method for finding solutions of linear systems Ax = b. We point out that the arising sequence (xk)k=1∞ tends to converge to the solution x in an interesting way: generically, as k rightarrow ∞, xk - x tends to the singular vector of A corresponding to the smallest singular value. This has interesting consequences: in particular, the error analysis of Strohmer & Vershynin is optimal. It also quantifies the `pre-convergence' phenomenon where the method initially seems to converge faster. This fact also allows for a fast computation of vectors x for which the Rayleigh quotient |Ax|/|x| is small: solve Ax = 0 via Randomized Kaczmarz.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 30 Jun 2020.
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Citing papers per year: 2021: 1; 2022: 3; 2023: 0; 2024: 1; 2025: 2; 2026: 2.
9 papers.
- 2021SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticalitycited by 33
- 2022Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decompositioncited by 17
- 2022Side Effects of Learning from Low-dimensional Data Embedded in a Euclidean Spacecited by 6
- 2024Randomized Kaczmarz with geometrically smoothed momentumcited by 3
- 2025Turbocharging Gaussian Process Inference with Approximate Sketch-and-Projectcited by 3
- 2022An optimal scheduled learning rate for a randomized Kaczmarz algorithmcited by 1
- 2025Where Have All the Kaczmarz Iterates Gone?cited by 0
- 2026From Order to Distribution: An Exact Operator Framework for Forgetting in Continual Learningcited by 0
- 2026Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spacescited by 0
- 2026Complex Stochastic Gradient Descent and Directional Bias in Reproducing Kernel Hilbert Spaces24 Apr 2026
- 2026From Order to Distribution: An Exact Operator Framework for Forgetting in Continual Learning15 Apr 2026
- 2025Where Have All the Kaczmarz Iterates Gone?9 Oct 2025
- 2025Turbocharging Gaussian Process Inference with Approximate Sketch-and-Project19 May 2025
- 2024Randomized Kaczmarz with geometrically smoothed momentum17 Jan 2024
- 2022Sharp Analysis of Sketch-and-Project Methods via a Connection to Randomized Singular Value Decomposition20 Aug 2022
- 2022Side Effects of Learning from Low-dimensional Data Embedded in a Euclidean Space1 Mar 2022
- 2022An optimal scheduled learning rate for a randomized Kaczmarz algorithm24 Feb 2022
- 2021SGD in the Large: Average-case Analysis, Asymptotics, and Stepsize Criticality8 Feb 2021