Department of Mathematics,
University of California San Diego
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Center for Computational Mathematics Seminar
Robert Webber
UCSD
Randomized least-squares solvers
Abstract:
Many data science problems require solving a least-squares problem min_x || A x - b ||^2. Efficiently solving this problem becomes a challenge when A has millions of rows, or even higher. I am developing solutions based on randomized numerical linear algebra:
1. If A is small enough to fit in working memory, an efficient solution is conjugate gradient with randomized preconditioning.
2. If A is too large to fit in working memory but x fits in memory, an intriguing possibility is randomized Kaczmarz.
3. If x is too large to fit in working memory, the final possibility is randomly sparsified Richardson iteration.
February 4, 2025
11:00 AM
APM 2402 and Zoom ID 946 7260 9849
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