Eigenpair extractions are crucial for various applications in geometry processing and graphics. State of the Art libraries like ARPACK or Spectra rely on the implicitly restarted Lanczos iteration to extract eigenpairs efficiently. However for some large scale problems they lack convergence speed and robustness. In this paper we present a simple multigrid extension to accelerate the convergence and robustness of the implicitly restarted Lanczos method, and we demonstrate the efficiency of our method on a variety of problems commonly found in geometry processing and graphics.
@article{BDT2026MultiscaleLanczos,
author = {Braune, Theo and Dumas, Jeremie and Thiery, Jean-Marc},
title = {Efficient Multiscale Lanczos Eigenpair Extraction},
year = {2026},
issue_date = {July 2026},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {45},
number = {5},
url = {https://doi.org/10.1145/3811367},
doi = {10.1145/3811367},
abstract = {Eigenpair extractions are crucial for various applications in geometry processing
and graphics. State of the Art libraries like ARPACK or Spectra rely
on the implicitly restarted Lanczos iteration to extract eigenpairs efficiently.
However for some large scale problems they lack convergence speed and robustness.
In this paper we present a simple multigrid extension to accelerate
the convergence and robustness of the implicitly restarted Lanczos method,
and we demonstrate the efficiency of our method on a variety of problems
commonly found in geometry processing and graphics.},
journal = {ACM Trans. Graph.},
month = jul,
articleno = {5},
numpages = {14},
keywords = {Linear Algebra, Eigenpair extraction, Multigrid, Lanczos}
}