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Topic
IMA Data Science Seminar - Jianfeng Lu
Description
Speaker: Jianfeng Lu
Title: Coordinate Methods for Solving Eigenvalue Problems in High Dimensions
Abstract: The leading eigenvalue problem of a differential operator
arises in many scientific and engineering applications, in particular
quantum many-body problems. Due to the curse of dimensionality,
conventional algorithms become impractical due to the huge computational
and memory complexity. In this talk, we will discuss some of our recent
works on developing efficient coordinate based approaches for eigenvalue
problems in high dimension and on convergence analysis of randomized
coordinate algorithms based on the theory of random dynamical systems.
(joint work with Ziang Chen, Yingzhou Li, and Zhe Wang)
Time
Mar 2, 2021 01:25 PM in
Central Time (US and Canada)
Webinar is over, you cannot register now. If you have any questions, please contact Webinar host:
Georgia M Kroll
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Hi there, You are invited to a Zoom webinar. When: Mar 2, 2021 01:25 PM Central Time (US and Canada) Topic: IMA Data Science Seminar - Jianfeng Lu Register in advance for this webinar: https://umn.zoom.us/webinar/register/WN_h9BnfbG5Q1O7IdJ7AI5pkQ Or an H.323/SIP room system: H.323: 162.255.37.11 (US West) 162.255.36.11 (US East) 221.122.88.195 (China) 115.114.131.7 (India Mumbai) 115.114.115.7 (India Hyderabad) 213.19.144.110 (Amsterdam Netherlands) 213.244.140.110 (Germany) 103.122.166.55 (Australia Sydney) 103.122.167.55 (Australia Melbourne) 209.9.211.110 (Hong Kong SAR) 149.137.40.110 (Singapore) 64.211.144.160 (Brazil) 69.174.57.160 (Canada Toronto) 65.39.152.160 (Canada Vancouver) 207.226.132.110 (Japan Tokyo) 149.137.24.110 (Japan Osaka) Meeting ID: 915 2724 7440 SIP: 91527247440@zoomcrc.com After registering, you will receive a confirmation email containing information about joining the webinar. ---------- Webinar Speakers Jianfeng Lu (Professor @Duke University) Jianfeng Lu is a Professor of Mathematics, Physics, and Chemistry at Duke University. Before joining Duke University, he obtained his PhD in Applied Mathematics from Princeton University in 2009 and was a Courant Instructor at New York University from 2009 to 2012. He works on mathematical analysis and algorithm development for problems and challenges arising from computational physics, theoretical chemistry, materials science, high-dimensional PDEs, and machine learning. His work has been recognized by a Sloan Fellowship, a NSF Career Award, and the 2017 IMA Prize in Mathematics and its Applications.
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