This article may rely excessively on sources
too closely associated with the subject, potentially preventing the article from being
verifiable and
neutral. (January 2016) |
Developer(s) |
|
---|---|
Stable release | 3.4
[1]
/ 18 August 2021 |
Written in | C++ |
Operating system | Cross-platform |
Type | Library |
License | MPL 2.0 |
Website |
eigen |
Eigen is a high-level C++ library of template headers for linear algebra, matrix and vector operations, geometrical transformations, numerical solvers and related algorithms. Eigen is open-source software licensed under the Mozilla Public License 2.0 since version 3.1.1. Earlier versions were licensed under the GNU Lesser General Public License. [2] Version 1.0 was released in Dec 2006. [3]
Eigen is implemented using the expression templates metaprogramming technique, meaning it builds expression trees at compile time and generates custom code to evaluate these. Using expression templates and a cost model of floating point operations, the library performs its own loop unrolling and vectorization. [4] Eigen itself can provide BLAS and a subset of LAPACK interfaces. [5]
Release 3.4 (2021) includes many improvements. [6]
The eigen_blas library is complete. The eigen_lapack currently implements cholesky and lu decomposition. Contact us if you want to help.
This article may rely excessively on sources
too closely associated with the subject, potentially preventing the article from being
verifiable and
neutral. (January 2016) |
Developer(s) |
|
---|---|
Stable release | 3.4
[1]
/ 18 August 2021 |
Written in | C++ |
Operating system | Cross-platform |
Type | Library |
License | MPL 2.0 |
Website |
eigen |
Eigen is a high-level C++ library of template headers for linear algebra, matrix and vector operations, geometrical transformations, numerical solvers and related algorithms. Eigen is open-source software licensed under the Mozilla Public License 2.0 since version 3.1.1. Earlier versions were licensed under the GNU Lesser General Public License. [2] Version 1.0 was released in Dec 2006. [3]
Eigen is implemented using the expression templates metaprogramming technique, meaning it builds expression trees at compile time and generates custom code to evaluate these. Using expression templates and a cost model of floating point operations, the library performs its own loop unrolling and vectorization. [4] Eigen itself can provide BLAS and a subset of LAPACK interfaces. [5]
Release 3.4 (2021) includes many improvements. [6]
The eigen_blas library is complete. The eigen_lapack currently implements cholesky and lu decomposition. Contact us if you want to help.