Full linear multistep methods as root-finders
For researchers in numerical analysis, this work provides a new perspective on using LMMs for root-finding, though the practical impact is limited as the method is incremental and no SOTA comparisons are given.
The paper introduces root-finders based on full linear multistep methods (LMMs), demonstrating that they circumvent zero-stability issues and achieve excellent performance in numerical examples. A fundamental barrier on convergence rate is proven via inverse polynomial interpolation, and a robust implementation using Brent's method is provided.
Root-finders based on full linear multistep methods (LMMs) use previous function values, derivatives and root estimates to iteratively find a root of a nonlinear function. As ODE solvers, full LMMs are typically not zero-stable. However, used as root-finders, the interpolation points are convergent so that such stability issues are circumvented. A general analysis is provided based on inverse polynomial interpolation, which is used to prove a fundamental barrier on the convergence rate of any LMM-based method. We show, using numerical examples, that full LMM-based methods perform excellently. Finally, we also provide a robust implementation based on Brent's method that is guaranteed to converge.