Routines for Matrix Inversion

It is seldom necessary to compute an explicit inverse of a matrix. In particular, do not attempt to solve a system of equations Ax = b by first computing A^-1 and then forming the matrix-vector product x = A^-1b. Call a solver routine instead (see Routines for Solving Systems of Linear Equations); this is more efficient and more accurate.

However, matrix inversion routines are provided for the rare occasions when an explicit inverse matrix is needed.

?getri
Computes the inverse of an LU-factored general matrix.
?potri
Computes the inverse of a symmetric (Hermitian) positive-definite matrix.
?pftri
Computes the inverse of a symmetric (Hermitian) positive-definite matrix in RFP format using the Cholesky factorization.
?pptri
Computes the inverse of a packed symmetric (Hermitian) positive-definite matrix
?sytri
Computes the inverse of a symmetric matrix.
?hetri
Computes the inverse of a complex Hermitian matrix.
?sytri2
Computes the inverse of a symmetric indefinite matrix through setting the leading dimension of the workspace and calling ?sytri2x.
?hetri2
Computes the inverse of a Hermitian indefinite matrix through setting the leading dimension of the workspace and calling ?hetri2x.
?sytri2x
Computes the inverse of a symmetric indefinite matrix after ?sytri2 sets the leading dimension of the workspace.
?hetri2x
Computes the inverse of a Hermitian indefinite matrix after ?hetri2 sets the leading dimension of the workspace.
?sptri
Computes the inverse of a symmetric matrix using packed storage.
?hptri
Computes the inverse of a complex Hermitian matrix using packed storage.
?trtri
Computes the inverse of a triangular matrix.
?tftri
Computes the inverse of a triangular matrix stored in the Rectangular Full Packed (RFP) format.
?tptri
Computes the inverse of a triangular matrix using packed storage.

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