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df = fndir(f,y)
df = fndir(f,y) is the
ppform of the directional derivative, of the function
in f,
in the direction of the (column-)vector y. This
means that df describes the function
.
If y is a matrix, with n columns,
and
is d-valued, then the function
in df is prod(d)*n-valued. Its
value at
, reshaped to be of size [d,n],
has in its
th `column' the directional derivative of
at
in the direction of the
th column of y.
If you prefer df to reflect explicitly the actual
size of
, use instead
df = fnchg( fndir(f,y), 'dim',[fnbrk(f,'dim'),size(y,2)] );
Since fndir relies on the ppform of the function in f, it does not work for rational functions nor for functions in stform.
For example, if f describes an m-variate d-vector-valued function and x is some point in its domain, then, e.g., with this particular ppform f that describes a scalar-valued bilinear polynomial,
f = ppmak({0:1,0:1},[1 0;0 1]); x = [0;0];
[d,m] = fnbrk(f,'dim','var');
jacobian = reshape(fnval(fndir(f,eye(m)),x),d,m)
is the Jacobian of that function at that point (which, for this particular scalar-valued function, is its gradient, and it is zero at the origin).
As a related example, the next statements plot the gradients of (a good approximation to) the Franke function at a regular mesh:
xx = linspace(-.1,1.1,13); yy = linspace(0,1,11);
[x,y] = ndgrid(xx,yy); z = franke(x,y);
pp2dir = fndir(csapi({xx,yy},z),eye(2));
grads = reshape(fnval(pp2dir,[x(:) y(:)].'),...
[2,length(xx),length(yy)]);
quiver(x,y,squeeze(grads(1,:,:)),squeeze(grads(2,:,:)))
Here is the resulting plot.

The function in f is converted to ppform, and the directional derivative of its polynomial pieces is computed formally and in one vector operation, and put together again to form the ppform of the directional derivative of the function in f.
![]() | fnder | fnint | ![]() |

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