Piecewise Cubic Hermite Interpolating Polynomial (PCHIP)


yi = pchip(x,y,xi)
pp = pchip(x,y)


yi = pchip(x,y,xi) returns vector yi containing elements corresponding to the elements of xi and determined by piecewise cubic interpolation within vectors x and y. The vector x specifies the points at which the data y is given, so x and y must have the same length. If y is a matrix or array, then the values in the last dimension, y(:,...,:,j), are taken as the values to match with x. In that case, the last dimension of y must be the same length as x. If y has n dimensions, then output yi is of size [size(y,1) size(y,2) ... size(y,n-1) length(xi)]. For example, if y is a matrix, then yi is of size [size(y,1) length(xi)].

pp = pchip(x,y) returns a piecewise polynomial structure for use by ppval. x can be a row or column vector. y is a row or column vector of the same length as x, or a matrix with length(x) columns.

pchip finds values of an underlying interpolating function P(x) at intermediate points, such that:

  • On each subinterval xkxxk+1, is the cubic Hermite interpolant to the given values and certain slopes at the two endpoints.

  • P(x) interpolates y, i.e., P(xj)=yj, and the first derivative dPdx is continuous. The second derivative d2Pdx2 is probably not continuous; there may be jumps at the xj.

  • The slopes at the xj are chosen in such a way that preserves the shape of the data and respects monotonicity. This means that, on intervals where the data are monotonic, so is ; at points where the data has a local extremum, so does .

    Note   If y is a matrix, satisfies the above for each column of y.


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Data Interpolation Using spline and pchip

x = -3:3;
y = [-1 -1 -1 0 1 1 1];
t = -3:.01:3;
p = pchip(x,y,t);
s = spline(x,y,t);

More About

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spline constructs S(x) in almost the same way pchip constructs . However, spline chooses the slopes at the xj differently, namely to make even S(x) continuous. This has the following effects:

  • spline produces a smoother result, i.e. S(x) is continuous.

  • spline produces a more accurate result if the data consists of values of a smooth function.

  • pchip has no overshoots and less oscillation if the data are not smooth.

  • pchip is less expensive to set up.

  • The two are equally expensive to evaluate.


[1] Fritsch, F. N. and R. E. Carlson, "Monotone Piecewise Cubic Interpolation," SIAM J. Numerical Analysis, Vol. 17, 1980, pp.238-246.

[2] Kahaner, David, Cleve Moler, Stephen Nash, Numerical Methods and Software, Prentice Hall, 1988.

See Also

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Introduced before R2006a

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