F# for Numerics

All the numerical methods you will ever need in a single beautifully-integrated F# library with an elegant functional interface:

  • Easy to use!
  • Matrix factorizations including inversion, SVD, eigenvalues and eigenvectors.
  • Spectral methods including the Fast Fourier Transform (FFT).
  • Numerical integration and differentiation.
  • Interpolation, curve fitting and regression.
  • Function minimization/optimization.
  • Mean, median, mode, variance, standard deviation, Shannon entropy and other statistical quantities.

This library leverages the awesome power of Microsoft's F# programming language for technical computing, allowing you to solve your problems quickly and easily.

Beta Release Scheme

Get development releases now, impact the product's evolution and get a free upgrade to the single-user version 1.0 worth £99!

Redistributable DLL

For commercial users building products upon this library.

Source code license

Buy a license to obtain and use the entire source code up to the first full release in your company or institution!

Free registration

If you have any comments or suggestions about our F# for Numerics library or would just like to know about updates, please register your interest.

Special Offers

For a limited time only, buy the F# for Technical Computing book, a one year subscription to the F#.NET Journal and the F# for Numerics and F# for Visualization libraries and get over 20% off!

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Buy the F# for Technical Computing book and the F# for Numerics and F# for Visualization libraries and get over 20% off!

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Buy the F# for Numerics and F# for Visualization libraries and a one year subscription to the F#.NET Journal and get 25% off!

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Subscribe to our beta release schemes for F# for Visualization and F# for Numerics at the same time and get 20% off!

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Customer Testimonials

"Your products are spectacular..." - Michael Garrett, Garrett Technologies.

Full feature list

When complete, this library will implement the following functionality:

General features

  • Automatic parallelization to exploit multicore systems.
  • Custom run-time compilation for the best possible performance on any given problem.
  • Seamless integration with the F# for Visualization library

Functional methods

  • Special functions.
  • Interpolation and extrapolation.
  • Random number generation.
  • Numerical integration.
  • Root finding.
  • Function minimization and maximization.

Spectral methods

  • Fast Fourier transforms.
  • Easy-to-use frequency interpretations.
  • Convolutions.

Vectors, matrices and linear algebra

  • Matrix inversion.
  • Eigenvalues and eigenvectors.
  • Cholesky decomposition.
  • Singular value decomposition.
  • LU decomposition.
  • QR decomposition.

Statistics

  • Mean, median and modal averages.
  • Variance and standard deviation.
  • Skew and kurtosis.
  • Shannon entropy.
  • Curve fitting and regression.

Current features

The current release provides many useful numerical methods:

  • Cholesky decomposition of real and complex positive definite matrices.
  • Matrix inversion for real, complex and arbitrary-precision rational matrices.
  • LU decomposition with partial pivoting of real, complex and arbitrary-precision rational matrices and an easy-to-use linear solver that effectively multiplies a vector by the inverse of a matrix.
  • Eigenvalue and eigenvector computation for real symmetric matrices.
  • Singular value decomposition of real matrices.
  • 1D and 2D Fast Fourier transforms: O(n log n) for any n.
  • Linear, cubic spline and Lagrange polynomial function interpolation.
  • Mersenne Twister random number generator with random number generators over six numeric types and over the Normal (Gaussian) distribution.
  • Special functions: j0 and j1 Bessel functions, beta function, error function (erf), gamma function, log gamma function, gaussian (normal distribution), heaviside step function, kronecker delta function, probit function and sinc.
  • Numerical derivatives and gradients.
  • Function minimization/optimization.
  • Mean, median, mode, variance, standard deviation, skewness, kurtosis and Shannon entropy.
  • Dozens of physical constants from subatomic particles to stellar phenomena.

On-line documentation

The HTML documentation for the current beta release of F# for Numerics is now available on-line here.

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