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1. Speed up exp(x) by reducing the polynomial approximant from degree 7 to degree 6. With exactly representable coefficients computed by the Sollya tool, this still gives a maximum relative error of 1 ulp, i.e. faithfully rounded, for arguments where exp(x) is a normalized float. This change results in a speedup of about 4% for AVX2. 2. Extend the range where exp(x) returns a non-zero result to from ~[-88;88] to ~[-104;88] i.e. return denormalized values for large negative arguments instead of zero. Compared to exp<double>(x) the denormalized results gradually decrease in accuracy down to 0.033 relative error for arguments around x = -104 where exp(x) is ~std::numeric<float>::denorm_min(). This is expected and acceptable.
Eigen is a C++ template library for linear algebra: matrices, vectors, numerical solvers, and related algorithms.
For more information go to http://eigen.tuxfamily.org/.
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