EIGENMATH USER GUIDE SOFTWARE
EIGENMATH USER GUIDE CODE
EIGENMATH USER GUIDE LICENSE
EIGENMATH USER GUIDE FREE

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To get help, stackoverflow is your best resource.See this page for some known compilation issues. Regarding performance, Eigen performs best with compilers based on GCC or LLVM/Clang. (The 2.8 version used to work fine, but it is not tested with up-to-date versions of Eigen) Enabling the -inline-forceinline option is highly recommended. The old 3.2 version of Eigen supports MSVC 2010, and the 3.1 version supports MSVC 2008. Be aware that enabling IntelliSense (/FR flag) is known to trigger some internal compilation errors. Older versions of gcc might work as well but they are not tested anymore. Whenever we use some non-standard feature, that is optional and can be disabled.Įigen is being successfully used with the following compilers: See the MPL2 FAQ for more information, and do not hesitate to contact us if you have any questions.Įigen is standard C++98 and so should theoretically be compatible with any compliant compiler.

EIGENMATH USER GUIDE SOFTWARE

Many proprietary and closed-source software projects are using Eigen right now, as well as many BSD-licensed projects. For example, closed-source software may use Eigen without having to disclose its own source code.

EIGENMATH USER GUIDE LICENSE

Of course you have to mind the license of the so-included library when using them. Such features can be explicitly disabled by compiling with the EIGEN_MPL2_ONLY preprocessor symbol defined.įurthermore, Eigen provides interface classes for various third-party libraries (usually recognizable by the header name).

EIGENMATH USER GUIDE CODE

Note that currently, a few features rely on third-party code licensed under the LGPL: constrained_cg. Common questions about the MPL2 are answered in the official MPL2 FAQ.Įarlier versions were licensed under the LGP元+. Starting from the 3.1.1 version, it is licensed under the MPL2, which is a simple weak copyleft license.

EIGENMATH USER GUIDE FREE

Eigen is a pure template library defined in the headers.Įigen is Free Software. There is no binary library to link to, and no configured header file. If you just want to use Eigen, you can use the header files right away.

We use the CMake build system, but only to build the documentation and unit-tests, and to automate installation.

Eigen 2 documentation (old): this includes the Eigen 2 Tutorial.Įigen doesn't have any dependencies other than the C++ standard library.

Eigen 3 documentation: this includes a getting started guide, a long tutorial, a quick reference, and page about porting from Eigen 2 to Eigen 3.

Eigen up to version 3.4 is standard C++03 and maintains reasonable compilation times.

Eigen has good compiler support as we run our test suite against many compilers to guarantee reliability and work around any compiler bugs.

Implementing an algorithm on top of Eigen feels like just copying pseudocode.

The API is extremely clean and expressive while feeling natural to C++ programmers, thanks to expression templates.

Eigen is thoroughly tested through its own test suite (over 500 executables), the standard BLAS test suite, and parts of the LAPACK test suite.

Reliability trade-offs are clearly documented and extremely safe decompositions are available.

Algorithms are carefully selected for reliability.

For large matrices, special attention is paid to cache-friendliness.

Fixed-size matrices are fully optimized: dynamic memory allocation is avoided, and the loops are unrolled when that makes sense.

Explicit vectorization is performed for SSE 2/3/4, AVX, AVX2, FMA, AVX512, ARM NEON (32-bit and 64-bit), PowerPC AltiVec/VSX (32-bit and 64-bit), ZVector (s390x/zEC13) SIMD instruction sets, and since 3.4 MIPS MSA with graceful fallback to non-vectorized code.

Expression templates allow intelligently removing temporaries and enable lazy evaluation, when that is appropriate.

Its ecosystem of unsupported modules provides many specialized features such as non-linear optimization, matrix functions, a polynomial solver, FFT, and much more.

It supports various matrix decompositions and geometry features.

It supports all standard numeric types, including std::complex, integers, and is easily extensible to custom numeric types.

It supports all matrix sizes, from small fixed-size matrices to arbitrarily large dense matrices, and even sparse matrices.

Eigenmath user guide