property GPCCA.schur_matrix: Optional[numpy.ndarray]

Schur matrix.

The real Schur decomposition is a generalization of the Eigendecomposition and can be computed for any real-valued, square matrix \(A\). It is given by \(A = Q R Q^T\), where \(Q\) contains the real Schur vectors and \(R\) is the Schur matrix. \(Q\) is orthogonal and \(R\) is quasi-upper triangular with 1x1 and 2x2 blocks on the diagonal. If PETSc and SLEPc are installed, only the leading Schur vectors are computed.

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