Arnoldi

基本解釋

For nonlinear model order reductions, three new algorithms based on quadratic method and Arnoldi process are proposed.
對于非線性系統(tǒng)的模型降階算法，本文將提出一類新的基于Taylor展開的二次化方法與Arnoldi過程相結(jié)合的算法。
For linear model reduction algorithms, The Krylov subspace based algorithms including Arnoldi process and Lanczos algorithm are introduced.
同時(shí)對于這兩種常見的模型降階算法，本文將給出相關(guān)的誤差分析。
In order to improve the quality of the Ritz vectors, in Chapter 4 a strategy proposed by Jia and Elsner is generalized to the block Arnoldi version.
為了改進(jìn)Ritz向量的性能，在第四章中我們將Jia和Elsner的一種策略推廣到塊Arnoldi情形。
The efficiency of the Jacobi-Davidson method at initial iteration steps is improved by using the Arnoldi decomposition to construct the initial orthogonal subspace.
針對Jacobi-Davidson方法在迭代初期效率低下的問題，提出了用Arnoldi分解構(gòu)造正交子空間作為初始子空間的方案，有效地改善了迭代初期的計(jì)算效率。
As to the Power Ground RLC equivalent circuit with a large number of independent current sources, we propose a novel HOAR (High Order ARnoldi) based MOR method.
在電源地網(wǎng)絡(luò)RLC等效電路降階分析方面，我們提出了一種新的基于高次Arnoldi過程的降階算法HOAR。