Abstract
This paper proposes a new method for solving the inversion of the system matrix that appears in the process of numerical integration of matrix dynamic equation. The singular value decomposition (SVD) has been widely known to solve the inversion of the matrix or to solve the Riccatti type matrix equation. The prominent advantage of using the SVD method resides in the fact that it provides the singular values that can be used for stabilizing the computation. However its low convergence rate prevents it to be used in the applications that handle a large-scale system matrix or in the real time dynamic analysis. The fast similarity factorization (FSF) proposed in the paper is one kind of SVD in a sense that it consists of many times orthogonal transformations. But the FSF provides fast and stable singular value decomposition. The simulation shown in this paper reveals its overwhelming convergence rate compared to the conventional SVD algorithms
