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Randomized least-squares solvers
Robert Webber
UCSD
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.
Tuesday, February 4, 2025
11:00AM AP&M 2402 and Zoom ID 946 7260 9849
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Randolph E. Bank
Philip E. Gill
Michael Holst
Administrative Contact:
[ Click to Send Email ]
Robert Webber
UCSD
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.
Tuesday, February 4, 2025
11:00AM AP&M 2402 and Zoom ID 946 7260 9849