"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.
