Rounding Error Analysis of Mixed Precision Block Householder QR Algorithms
L. Minah Yang, Alyson Fox, Geoffrey D. Sanders · SIAM Journal on Scientific Computing · 2021
Although mixed precision arithmetic has recently garnered interest for training dense neural networks, many other applications could benefit from the speedups and lower storage cost if applied appropriately. The growing interest in employing mixed precision computations motivates the need for rounding error analysis that properly handles behavior from mixed precision arithmetic. We develop mixed precision variants of existing Householder QR algorithms and show error analyses supported by numerical experiments.