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## 27.1 Evaluating Polynomials

The value of a polynomial represented by the vector c can be evaluated at the point x very easily, as the following example shows:

 ```N = length(c)-1; val = dot( x.^(N:-1:0), c ); ```

While the above example shows how easy it is to compute the value of a polynomial, it isn't the most stable algorithm. With larger polynomials you should use more elegant algorithms, such as Horner's Method, which is exactly what the Octave function `polyval` does.

In the case where x is a square matrix, the polynomial given by c is still well-defined. As when x is a scalar the obvious implementation is easily expressed in Octave, but also in this case more elegant algorithms perform better. The `polyvalm` function provides such an algorithm.

Function File: y = polyval (p, x)
Function File: y = polyval (p, x, [], mu)

Evaluate the polynomial at of the specified values for x. When mu is present evaluate the polynomial for (x-mu(1))/mu(2). If x is a vector or matrix, the polynomial is evaluated for each of the elements of x.

Function File: [y, dy] = polyval (p, x, s)
Function File: [y, dy] = polyval (p, x, s, mu)

In addition to evaluating the polynomial, the second output represents the prediction interval, y +/- dy, which contains at least 50% of the future predictions. To calculate the prediction interval, the structured variable s, originating form `polyfit', must be present.

See also: polyfit, polyvalm, poly, roots, conv, deconv, residue, filter, polyderiv, polyinteg.

Function File: polyvalm (c, x)

Evaluate a polynomial in the matrix sense.

`polyvalm (c, x)` will evaluate the polynomial in the matrix sense, i.e., matrix multiplication is used instead of element by element multiplication as is used in polyval.

The argument x must be a square matrix.

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