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.

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