Interpolation
Namespace: MathNet.Numerics.Interpolation
Interpolation is a two-phased operation in Math.NET Numerics:
- Create an interpolation scheme for the chosen algorithm and optimized for the given sample points. You get back a class that implements the IInterpolation interface.
- Use this scheme to compute values at arbitrary points. Some interpolation algorithms also allow you to compute the derivative and the indefinite integral at that point.
The static Interpolate class provides simple factory methods to create the interpolation scheme in a simple method call:
- RationalWithoutPoles, creates a Floater-Hormann barycentric interpolation
- RationalWithPoles, creates a Bulirsch & Stoer rational interpolation
- LinearBetweenPoints, creates a linear spline interpolation
If unsure, we recommend using RationalWithoutPoles for most cases.
Alternatively you can also use the algorithms directly, they're publicly available in the Algorithms sub-namespace for those who want to use a specific algorithm. The following algorithms are available:
Interpolation on equidistant sample points
- Polynomial: Barycentric Algorithm
Interpolation on arbitrary sample points
Rational pole-free: Barycentric Floater-Hormann Algorithm
Rational with poles: Bulirsch & Stoer Algorithm
Neville Polynomial: Neville Algorithm. Note that the Neville algorithm performs very badly on equidistant points. If you need to interpolate a polynomial on equidistant points, we recommend to use the barycentric algorithm instead.
Linear Spline
Cubic Spline with boundary conditions
Natural Cubic Spline
Akima Cubic Spline
Interpolation with additional data
Generic Barycentric Interpolation, requires barycentric weights
Generic Spline, requires spline coefficients
Generic Cubic Hermite Spline, requires the derivatives