A Padé approximate linearization algorithm for solving the quadratic eigenvalue problem with low‐rank damping
Ding Lu, Xin Sunny Huang, Zhaojun Bai, Yangfeng Su · International Journal for Numerical Methods in Engineering · 2015
Summary The low‐rank damping term appears commonly in quadratic eigenvalue problems arising from physical simulations. To exploit the low‐rank damping property, we propose a Padé approximate linearization (PAL) algorithm. The advantage of the PAL algorithm is that the dimension of the resulting linear eigenvalue problem is only n + ℓ m , which is generally substantially smaller than the dimension 2 n of the linear eigenvalue problem produced by a direct linearization approach, where n is the dimension of the quadratic eigenvalue problem, and ℓ and m are the rank of the damping matrix and the order of a Padé approximant, respectively. Numerical examples show that by exploiting the low‐rank damping property, the PAL algorithm runs 33–47% faster than the direct linearization approach for solving modest size quadratic eigenvalue problems. Copyright © 2015 John Wiley & Sons, Ltd.