Searching for Rare Growth Factors Using Multicanonical Monte Carlo Methods

The growth factor of a matrix quantifies the amount of potential error growth possible when a linear system is solved using Gaussian elimination with row pivoting. While it is an easy matter [N. J. Higham and D. J. Higham, SIAM J. Matrix Anal. Appl., 10 (1989), pp. 155–164] to construct examples of n × n matrices having any growth factor up to the maximum of 2n−1, the weight of experience and analysis [N. J. Higham, Accuracy and Stability of Numerical Algorithms, SIAM, Philadelphia, 1996], [L. N. Trefethen and R. S. Schreiber, SIAM J. Matrix Anal. Appl., 11 (1990), pp. 335–360], [L. N. Trefethen and I. D. Bau, Numerical Linear Algebra, SIAM, Philadelphia, 1997] suggest that matrices with exponentially large growth factors are exceedingly rare. Here we show how to conduct numerical experiments on random matrices using a multicanonical Monte Carlo method to explore the tails of growth factor probability distributions. Our results suggest, for example, that the occurrence of an 8× 8 matrix with a growth factor of 40 is on the order of a once-in-the-age-of-the-universe event.