2023/03/08 by Alan Edelman, John Urschel, Edelman, Alan +1
Computer Science · Mathematics · #15A23 #65F05 #Advanced Optimization Algorithms Research #FOS: Mathematics #Matrix Theory and Algorithms #Numerical Analysis (math.NA) #Numerical Methods and Algorithms
paper · pdf · doi:10.48550/arxiv.2303.04892
openalex publication_date 2023/03/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper combines modern numerical computation with theoretical results to improve our understanding of the growth factor problem for Gaussian elimination. On the computational side we obtain lower bounds for the maximum growth for complete pivoting for n=1:75 and n=100 using the Julia JuMP optimization package. At n=100 we obtain a growth factor bigger than 3n. The numerical evidence suggests that the maximum growth factor is bigger than n if and only if n ≥ 11. We also present a number of theoretical results. We show that the maximum growth factor over matrices with entries restricted to a subset of the reals is nearly equal to the maximum growth factor over all real matrices. We also show that the growth factors under floating point arithmetic and exact arithmetic are nearly identical. Finally, through numerical search, and stability and extrapolation results, we provide improved lower bounds for the maximum growth factor. Specifically, we find that the largest growth factor is bigger than 1.0045n for n>10, and the lim sup of the ratio with n is greater than or equal to 3.317. In contrast to the old conjecture that growth might never be bigger than n, it seems likely that the maximum growth divided by n goes to infinity as n → ∞.