"F# for Numerics" released

Flying Flog Consulting have recently published F# for Numerics. Here’s how they describe the library:

Our new F# for Numerics library is a suite of numerical methods that leverage functional programming with F#

This library implements numerical methods from a variety of different disciplines in a uniform way …:

  • Local and global function minimization and maximization.

  • Mean, median, mode, variance, standard deviation, skew, kurtosis, Shannon entropy and other statistical quantities.

  • Interpolation, curve fitting and regression.

  • Matrix factorizations including eigenvalue computation.

  • Numerical integration and differentiation.

  • Spectral methods including the Fast Fourier Transform.

The first update has reportedly added:

  • FFTs now 2× faster.

  • 1D FFTs over both arrays and vectors.

  • 2D FFTs over F# matrices with parallelism to exploit multicores.

  • Linear, cubic spline and Lagrange polynomial interpolation.

  • More special functions including sinc, the error function and the probit function.

  • Faster Mersenne Twister random number generation, particularly over the Normal distribution.

  • Physical constants.

  • More worked examples.

  • The binomial function for combinatorics.