- Package version:
- Home page:
- Source archive:
- Eigen is a C++ template library for linear algebra: vectors, matrices,
and related algorithms. It is:
* Versatile. (See modules and tutorial). Eigen handles, without code
duplication, and in a completely integrated way:
o both fixed-size and dynamic-size matrices and vectors.
o both dense and sparse (the latter is still experimental) matrices
o both plain matrices/vectors and abstract expressions.
o both column-major (the default) and row-major matrix storage.
o both basic matrix/vector manipulation and many more advanced,
specialized modules providing algorithms for linear algebra,
geometry, quaternions, or advanced array manipulation.
o various numeric types out of the box, including std::complex
numbers, while being easy to extend to custom numeric types.
* Fast. (See benchmark).
o Expression templates allow to intelligently remove temporaries and
enable lazy evaluation, when that is appropriate -- Eigen takes care
of this automatically and handles aliasing too in most cases.
o Explicit vectorization is performed for the SSE (2 and later),
AltiVec and ARM NEON (in the development branch for now) instruction
sets, with graceful fallback to non-vectorized code. Expression
templates allow to perform these optimizations globally for whole
o With fixed-size objects, dynamic memory allocation is avoided, and
the loops are unrolled when that makes sense.
o For large matrices, special attention is paid to cache-friendliness.
* Elegant. (See API showcase). The API is extremely clean and
expressive, thanks to expression templates. Implementing an
algorithm on top of Eigen feels like just copying pseudocode. You
can use complex expressions and still rely on Eigen to produce
optimized code: there is no need for you to manually decompose
expressions into small steps.
* Compiler-friendy. Eigen has very reasonable compilation times at
least with GCC, compared to other C++ libraries based on expression
templates and heavy metaprogramming. Eigen is also standard C++ and
supports various compilers.
- Run dependencies:
- Build dependencies:
- cmake>=2.6, digest>=20080510, g++>=3, gcc>=3, pax, pkg_install>=20110805.12, tnftp>=20130505~ssl
- General options:
- Produce debugging information for binary programs
- c++-compiler alternatives:
- Use the GNU C++ compiler
- Use the LLVM C++ compiler
- Use ccache and the GNU C++ compiler
- Use ccache and the LLVM C++ compiler
- c-compiler alternatives:
- Use the GNU C compiler
- Use the LLVM C compiler
- Use ccache and the GNU C compiler
- Use ccache and the LLVM C compiler