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Reliable Condition Number Estimation with Random Sampling: Scientific Computing and Matrix Computations Seminar

Seminar: Departmental | October 17 | 12:10-1 p.m. | 380 Soda Hall

Ming Gu, UC-Berkeley

Electrical Engineering and Computer Sciences (EECS)

Condition number estimation has traditionally been regarded as a 'quick and dirty' way to compute some estimation of the condition number of a given matrix. Typical condition number estimators usually do a good job of estimating the condition number to within a small factor, but can fail to estimate the condition number to any accuracy.
In this talk we show how to use randomized sampling to estimate condition numbers. Except a tiny failure probability, our algorithm computes reliable condition estimators. We also generalize this algorithm to efficiency and compute the p-norm of a given matrix.
Joint work with Chris Melgaard., 510-516-4321