Using BLAS/LAPACK from Eigen

Since Eigen version 3.3 and later, any F77 compatible BLAS or LAPACK libraries can be used as backends for dense matrix products and dense matrix decompositions. For instance, one can use Intel® MKL, Apple's Accelerate framework on OSX, OpenBLAS, Netlib LAPACK, etc.

Do not miss this page for further discussions on the specific use of Intel® MKL (also includes VML, PARDISO, etc.)

In order to use an external BLAS and/or LAPACK library, you must link you own application to the respective libraries and their dependencies. For LAPACK, you must also link to the standard Lapacke library, which is used as a convenient think layer between Eigen's C++ code and LAPACK F77 interface. Then you must activate their usage by defining one or multiple of the following macros (before including any Eigen's header):

Note

For Mac users, in order to use the lapack version shipped with the Accelerate framework, you also need the lapacke library. Using MacPorts, this is as easy as:

sudo port install lapack

Code

and then use the following link flags: -framework Accelerate /opt/local/lib/lapack/liblapacke.dylib

EIGEN_USE_BLAS	Enables the use of external BLAS level 2 and 3 routines (compatible with any F77 BLAS interface)
EIGEN_USE_LAPACKE	Enables the use of external Lapack routines via the Lapacke C interface to Lapack (compatible with any F77 LAPACK interface)
EIGEN_USE_LAPACKE_STRICT	Same as EIGEN_USE_LAPACKE but algorithms of lower numerical robustness are disabled. This currently concerns only JacobiSVD which otherwise would be replaced by gesvd that is less robust than Jacobi rotations.

When doing so, a number of Eigen's algorithms are silently substituted with calls to BLAS or LAPACK routines. These substitutions apply only for Dynamic or large enough objects with one of the following four standard scalar types: float, double, complex<float>, and complex<double>. Operations on other scalar types or mixing reals and complexes will continue to use the built-in algorithms.

The breadth of Eigen functionality that can be substituted is listed in the table below.

Functional domain	Code example	BLAS/LAPACK routines
Matrix-matrix operations EIGEN_USE_BLAS	m1*m2.transpose(); m1.selfadjointView<Lower>()*m2; m1*m2.triangularView<Upper>(); m1.selfadjointView<Lower>().rankUpdate(m2,1.0); Code	?gemm ?symm/?hemm ?trmm dsyrk/ssyrk Code
Matrix-vector operations EIGEN_USE_BLAS	m1.adjoint()*b; m1.selfadjointView<Lower>()*b; m1.triangularView<Upper>()*b; Code	?gemv ?symv/?hemv ?trmv Code
LU decomposition EIGEN_USE_LAPACKE EIGEN_USE_LAPACKE_STRICT	v1 = m1.lu().solve(v2); Code	?getrf Code
Cholesky decomposition EIGEN_USE_LAPACKE EIGEN_USE_LAPACKE_STRICT	v1 = m2.selfadjointView<Upper>().llt().solve(v2); Code	?potrf Code
QR decomposition EIGEN_USE_LAPACKE EIGEN_USE_LAPACKE_STRICT	m1.householderQr(); m1.colPivHouseholderQr(); Code	?geqrf ?geqp3 Code
Singular value decomposition EIGEN_USE_LAPACKE	JacobiSVD<MatrixXd, ComputeThinV> svd; svd.compute(m1); Code	?gesvd Code
Singular value decomposition EIGEN_USE_LAPACKE EIGEN_USE_LAPACKE_STRICT	BDCSVD<MatrixXd> svd; svd.compute(m1); Code	?gesdd Code
Eigen-value decompositions EIGEN_USE_LAPACKE EIGEN_USE_LAPACKE_STRICT	EigenSolver<MatrixXd> es(m1); ComplexEigenSolver<MatrixXcd> ces(m1); SelfAdjointEigenSolver<MatrixXd> saes(m1+m1.transpose()); GeneralizedSelfAdjointEigenSolver<MatrixXd> gsaes(m1+m1.transpose(),m2+m2.transpose()); Code	?gees ?gees ?syev/?heev ?syev/?heev, ?potrf Code
Schur decomposition EIGEN_USE_LAPACKE EIGEN_USE_LAPACKE_STRICT	RealSchur<MatrixXd> schurR(m1); ComplexSchur<MatrixXcd> schurC(m1); Code	?gees Code

In the examples, m1 and m2 are dense matrices and v1 and v2 are dense vectors.