The Schur decomposition is a representation of matrix *A* as *A = S ^{ T} T S*, where

This algorithm performs the Schur decomposition of the square matrix by using a QR algorithm with multiple shifts. The matrix is reduced to upper Hessenberg form. After that, algorithm of Schur decomposition of matrix in upper Hessenberg form is called. This algorithm is a block-matrix analog of the ordinary QR algorithm with double shift. As all other block-matrix algorithms, this algorithm requires adjustment to achieve optimal performance.

You can adjust a value of **NS** (internal parameter of the **InternalSchurDecomposition** subroutine) by defining a number of shifts in one iteration. Algorithm performance reachs its maximum between NS=4 and NS=16. If NS is more than 16, the algorithm slows down considerably. Interval ranges can differ according to different systems, but the situation is mostly same. The default value of NS provides a good performance on most systems, but if the performance is critical, it is worth adjusting this parameter manually. It should be noted that the optimal parameter value depends both on the hardware characteristics and on the properties of the matrices processed.

The **RMatrixSchur** subroutine performs the Schur decomposition. It returns matrices *T* and *S*. Commonly, we need to know the block structure of matrix T. It could be easily recognized: since all the elements below the blocks are zeros, elements a[i+1,i] which are equal to 0 show the block border.

*Algorithm implemented on the basis of DHSEQR subroutine (LAPACK 3.0 library).*

*This article is licensed for personal use only.*

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