Jin, B;
Zhou, Z;
Zou, J;
(2021)
An analysis of stochastic variance reduced gradient for linear inverse problems.
Inverse Problems
, 38
(2)
, Article 025009. 10.1088/1361-6420/ac4428.
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Abstract
Stochastic variance reduced gradient (SVRG) is a popular variance reduction technique for accelerating stochastic gradient descent (SGD). We provide a first analysis of the method for solving a class of linear inverse problems in the lens of the classical regularization theory. We prove that for a suitable constant step size schedule, the method can achieve an optimal convergence rate in terms of the noise level (under suitable regularity condition) and the variance of the SVRG iterate error is smaller than that by SGD. These theoretical findings are corroborated by a set of numerical experiments.
| Type: | Article |
|---|---|
| Title: | An analysis of stochastic variance reduced gradient for linear inverse problems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1088/1361-6420/ac4428 |
| Publisher version: | https://doi.org/10.1088/1361-6420/ac4428 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10140922 |
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