Abstract
The SR factorization for a given matrix A is a QR-like factorization A=SR, where the matrix S is symplectic and R is J-upper triangular. This factorization is fundamental for some important structure-preserving methods in linear algebra and is usually implemented via the symplectic Gram-Schmidt algorithm (SGS).
There exist two versions of SGS, the classical (CSGS) and the modified (MSGS). Both are equivalent in exact arithmetic, but have very different numerical behaviors. The MSGS is more stable. Recently, the symplectic Householder SR algorithm has been introduced, for computing efficiently the SR factorization. In this paper, we show two new and important results. The first is that the SR factorization of a matrix A via the MSGS is mathematically equivalent to the SR factorization via Householder SR algorithm of an embedded matrix. The later is obtained from A by adding two blocks of zeros in the top of the first half and in the top of the second half of the matrix A. The second result is that MSGS is also numerically equivalent to Householder SR algorithm applied to the mentioned embedded matrix.