Eigen

Software Code: 

Eigen

Version: 
3.3.3
Description: 
  • Eigen is versatile.
    • It supports all matrix sizes, from small fixed-size matrices to arbitrarily large dense matrices, and even sparse matrices.
    • It supports all standard numeric types, including std::complex, integers, and is easily extensible to custom numeric types.
    • It supports various matrix decompositions and geometry features.
    • Its ecosystem of unsupported modules provides many specialized features such as non-linear optimization, matrix functions, a polynomial solver, FFT, and much more.
  • Eigen is fast.
    • Expression templates allow to intelligently remove temporaries and enable lazy evaluation, when that is appropriate.
    • Explicit vectorization is performed for SSE 2/3/4, AVX, FMA, AVX512, ARM NEON (32-bit and 64-bit), PowerPC AltiVec/VSX (32-bit and 64-bit) instruction sets, and now S390x SIMD (ZVector) with graceful fallback to non-vectorized code.
    • Fixed-size matrices are fully optimized: dynamic memory allocation is avoided, and the loops are unrolled when that makes sense.
    • For large matrices, special attention is paid to cache-friendliness.
  • Eigen is reliable.
    • Algorithms are carefully selected for reliability. Reliability trade-offs are clearly documented and extremely safe decompositions are available.
    • Eigen is thoroughly tested through its own test suite (over 500 executables), the standard BLAS test suite, and parts of the LAPACK test suite.
  • Eigen is elegant.
    • The API is extremely clean and expressive while feeling natural to C++ programmers, thanks to expression templates.
    • Implementing an algorithm on top of Eigen feels like just copying pseudocode.
  • Eigen has good compiler support as we run our test suite against many compilers to guarantee reliability and work around any compiler bugs. Eigen also is standard C++98 and maintains very reasonable compilation times.
Research Area: 
Biology