LAPACK++  2022.07.00
LAPACK C++ API
Routines
Here is a list of all modules:
[detail level 12]
 Linear solve, AX = BSolve \(AX = B\)
 General matrix: LU
 General matrix: LU: banded
 General matrix: LU: tridiagonal
 Positive definite: Cholesky
 Positive definite: Cholesky: packed
 Positive definite: Cholesky: banded
 Positive definite: Cholesky: tridiagonal
 Symmetric indefinite
 Symmetric indefinite: packed
 Hermitian indefinite
 Hermitian indefinite: packed
 Linear solve: computational routinesFactor \(LU\), \(LL^H\), \(LDL^H\); solve; inverse; condition number estimate
 General matrix: LU
 General matrix: LU: banded
 General matrix: LU: tridiagonal
 Positive definite: Cholesky
 Positive definite: Cholesky: packed
 Positive definite: Cholesky: RFP
 Positive definite: Cholesky: banded
 Positive definite: Cholesky: tridiagonal
 Symmetric indefinite: Bunch-Kaufman
 Symmetric indefinite: Bunch-Kaufman: packed
 Symmetric indefinite: Rook
 Symmetric indefinite: Aasen's
 Hermitian indefinite: Bunch-Kaufman
 Hermitian indefinite: Bunch-Kaufman: packed
 Hermitian indefinite: Rook
 Hermitian indefinite: Aasen's
 Triangular
 Triangular: packed
 Triangular: RFP
 Triangular: banded
 Least squares
 Standard, AX = BSolve \(AX \approx B\)
 Constrained
 Orthogonal/unitary factorizations (QR, etc.)
 A = QR factorization
 A = QR factorization, triangle-pentagonal tiles
 AP = QR factorization with pivoting
 A = LQ factorization
 A = LQ factorization, triangle-pentagonal tiles
 A = QL factorization
 A = RQ factorization
 A = RZ factorization
 Generalized QR factorization
 Generalized RQ factorization
 Cosine-Sine (CS) decomposition
 Householder reflectors and plane rotations
 Symmetric/Hermitian eigenvalues
 Standard, AV = V Lambda
 Standard, AV = V Lambda: packed
 Standard, AV = V Lambda: banded
 Standard, AV = V Lambda: tridiagonal
 Generalized, AV = BV Lambda, etc.
 Generalized, AV = BV Lambda, etc.: packed
 Generalized, AV = BV Lambda, etc.: banded
 Computational routines
 Non-symmetric eigenvalues
 Standard, AV = V Lambda
 Generalized, AV = BV Lambda
 Schur form, A = ZTZ^H
 Generalized Schur form
 Computational routines
 Singular Value Decomposition (SVD)
 Standard, A = U Sigma V^H
 Standard, A = U Sigma V^H, bidiagonal
 Generalized
 Computational routines
 Auxiliary routines
 Initialize, copy, convert matrices
 Matrix norms
 Other auxiliary routines
 BLAS extensions in LAPACK
 symv: Symmetric matrix-vector multiply\(y = \alpha Ax + \beta y\)
 syr: Symmetric rank 1 update\(A = \alpha xx^T + A\)
 Test routines
 Test matrix generation
 Utilities