Randomized Algorithms for Preconditioner Selection with Applications to Kernel Regression
The task of choosing a preconditioner M to use when solving a linear system Ax=b with iterative methods is often tedious and most methods remain ad-hoc. This thesis presents a randomized algorithm to make this chore less painful through use of randomized algorithms for estimating traces. In particul...
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| Format: | Others |
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Scholarship @ Claremont
2019
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| Online Access: | https://scholarship.claremont.edu/hmc_theses/230 https://scholarship.claremont.edu/cgi/viewcontent.cgi?article=1229&context=hmc_theses |