Random sampling for estimating the performance of fast summations

In CS-TR-4969, University of Maryland, College Park, MD

Published October 18, 2010

Balaji Vasan Srinivasan, R. Duraiswami

Summation of functions of N source points evaluated at M target points occurs commonly in many applications. To scale these approaches for large datasets, many fast algorithms have been proposed. In this technical report, we propose a Chernoff bound based efficient approach to test the performance of a fast summation algorithms providing a probabilistic accuracy. We further validate and use our approach in separate comparisons.

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Research Area:  AI & Machine Learning