Eigensystem Analysis
Routines
2.1. Eigenvalue Decomposition
2.1.1 Computes the eigenvalues of a self-adjoint matrix
LIN_EIG_SELF2.1.2 Computes the eigenvalues of an n × n matrix
LIN_EIG_GEN2.1.3 Computes the generalized eigenvalues of an n × n matrix
pencil,
Av =
λBv LIN_GEIG_GEN2.2. Eigenvalues and (Optionally) Eigenvectors of Ax = λx
2.2.1 Real General Problem Ax = λx
All eigenvalues and eigenvectors
EVCRG2.2.2 Complex General Problem Ax = λx
All eigenvalues and eigenvectors
EVCCG2.2.3 Real Symmetric Problem Ax = λx
All eigenvalues and eigenvectors
EVCSFExtreme eigenvalues
EVASFExtreme eigenvalues and their eigenvectors
EVESFEigenvalues in an interval
EVBSFEigenvalues in an interval and their eigenvectors
EVFSF2.2.4 Real Band Symmetric Matrices in Band Storage Mode
All eigenvalues and eigenvectors
EVCSBExtreme eigenvalues
EVASBExtreme eigenvalues and their eigenvectors
EVESBEigenvalues in an interval
EVBSBEigenvalues in an interval and their eigenvectors
EVFSB2.2.5 Complex Hermitian Matrices
All eigenvalues and eigenvectors
EVCHFExtreme eigenvalues
EVAHFExtreme eigenvalues and their eigenvectors
EVEHFEigenvalues in an interval
EVBHFEigenvalues in an interval and their eigenvectors
EVFHF2.2.6 Real Upper Hessenberg Matrices
All eigenvalues and eigenvectors
EVCRH2.2.7 Complex Upper Hessenberg Matrices
All eigenvalues and eigenvectors
EVCCH2.3. Eigenvalues and (Optionally) Eigenvectors of Ax = λBx
2.3.1 Real General Problem Ax = λBx
All eigenvalues and eigenvectors
GVCRG2.3.2 Complex General Problem Ax = λBx
All eigenvalues and eigenvectors
GVCCG2.3.3 Real Symmetric Problem Ax = λBx
All eigenvalues and eigenvectors
GVCSP2.4. Eigenvalues and Eigenvectors Computed with ARPACK
Fortran 2003 Usage
Real singular value decomposition
AV = US ARPACK_SVDPublished date: 03/19/2020
Last modified date: 03/19/2020