September 17, 2019, 4:30 PM - 5:10 PM
Busch Campus Student Center
604 Bartholomew Rd
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Miles Lopes, University of California, Davis
In recent years, many randomized algorithms have been proposed for computing approximate solutions to large-scale problems in numerical linear algebra. However, the user rarely knows the actual error of a randomized solution. For this reason, it is common to rely on theoretical worst-case error bounds as a source of guidance. As a more practical alternative, we propose bootstrap methods to obtain direct error estimates for randomized solutions. Specifically, in the contexts of matrix multiplication and least-squares, we show that bootstrap error estimates are theoretically justified, and incur modest computational cost.
Joint work with Benjamin Erichson, Michael Mahoney, and Shusen Wang.