Speaker: Xin Guo
Affiliation: University of Queensland


Recently, the Randomized Kaczmarz algorithm (RK) draws much attention because of its low computational complexity and less requirement on computer memory. Many existing results on analysis focus on the behavior of RK in Euclidean spaces, and typically derive exponential converge rates with the base tending to one, as the condition number of the system increases. The dependence on the condition number largely restricts the application of these estimates. There are also results using relaxation (i.e., small step-sizes tending to zero as the sample size increases) to achieve polynomial convergence rates of RK in Hilbert spaces. In this work, we prove the weak convergence (with polynomial rates) of RK with constant step-size in Hilbert spaces. As a consequence, the strong convergence of RK in Euclidean spaces is obtained with condition number-free polynomial rates. In the setting with noisy data, we study the relaxation technique and obtain a strong convergence of RK in Hilbert spaces, with the rates arbitrarily close to the minimax optimal ones. We apply the analysis to reproducing kernel-based online gradient descent learning algorithms and improve the state-of-the-art convergence estimate in the literature.

About Maths Colloquium

The Mathematics Colloquium is directed at students and academics working in the fields of pure and applied mathematics, and statistics. 

We aim to present expository lectures that appeal to our wide audience.

Information for speakers

Information for speakers

Maths colloquia are usually held on Mondays, from 2pm to 3pm, in various locations at St Lucia.

Presentations are 50 minutes, plus five minutes for questions and discussion.

Available facilities include:

  • computer 
  • data projector
  • chalkboard or whiteboard

To avoid technical difficulties on the day, please contact us in advance of your presentation to discuss your requirements.


Priestley Building (67)
442 and Zoom (https://uqz.zoom.us/j/81688396546)