Comet Calendar

Computational Science Seminar by Jing Qin

Dial-In Information

Virtually via MS Teams at link below:

Friday, September 10, 2021 at 1:05pm to 2:00pm

Virtual Event

Regularized Kaczmarz Algorithms for Tensor Recovery

Jing Qin

Mathematics, U. of Kentucky

Abstract: Tensor recovery has recently arisen in a lot of application fields, such as transportation, medical imaging and remote sensing. Under the assumption that signals possess sparse and/or low-rank structures, many tensor recovery methods have been developed to apply various regularization techniques together with the operator-splitting type of algorithms. Due to the unprecedented growth of data, it becomes increasingly desirable to use streamlined algorithms to achieve real-time computation, such as stochastic optimization algorithms that have recently emerged as an efficient family of methods in machine learning. In this work, we propose a novel algorithmic framework based on the Kaczmarz algorithm for tensor recovery. We provide thorough convergence analysis and its applications from the vector case to the tensor one. Numerical results on a variety of tensor recovery applications, including sparse signal recovery, low-rank tensor recovery, image inpainting and deconvolution, illustrate the enormous potential of the proposed methods.

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Event Type

Lectures & Workshops

Target Audience

Graduate Students



Natural Sciences & Mathematics
Contact Information
John Zweck
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