Evaluating Polynomials - Untitled
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Next: Finding Roots, Up: Polynomial Manipulations

### 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.

> See also: polyval, poly, roots, conv, deconv, residue, filter, polyderiv, and polyinteg.