derivest

derivative of exp(x), at x == 0
DERIVEST can also use an inline function
Higher order derivatives (second derivative)
Higher order derivatives (third derivative)
Higher order derivatives (up to the fourth derivative)
Evaluate the indicated (default = first) derivative at multiple points
Specify the step size (default stepsize = 0.1)
Provide other parameters via an anonymous function
The second derivative should be positive at a minimizer.
Compute the numerical gradient vector of a 2-d function
Compute the numerical Laplacian function of a 2-d function
Compute the derivative of a function using a central difference scheme
Compute the derivative of a function using a forward difference scheme
Compute the derivative of a function using a backward difference scheme
Although a central rule may put some samples in the wrong places, it may still succeed
But forcing the use of a one-sided rule may be smart anyway
Control the behavior of DERIVEST - forward 2nd order method, with only 1 Romberg term
Functions should be vectorized for speed, but its not always easy to do.

% DERIVEST demo script

% This script file is designed to be used in cell mode
% from the matlab editor, or best of all, use the publish
% to HTML feature from the matlab editor. Older versions
% of matlab can copy and paste entire blocks of code into
% the Matlab command window.

% DERIVEST is property/value is driven for its arguments.
% Properties can be shortened to the

derivative of exp(x), at x == 0

[deriv,err] = derivest(@(x) exp(x),0)

deriv =
            1
err =
   1.4046e-14

DERIVEST can also use an inline function

[deriv,err] = derivest(inline('exp(x)'),0)

deriv =
            1
err =
   1.4046e-14

Higher order derivatives (second derivative)

Truth: 0

[deriv,err] = derivest(@(x) sin(x),pi,'deriv',2)

deriv =
  -5.5372e-19
err =
    1.865e-18

Higher order derivatives (third derivative)

Truth: 1

[deriv,err] = derivest(@(x) cos(x),pi/2,'der',3)

deriv =
            1
err =
   4.3657e-12

Higher order derivatives (up to the fourth derivative)

Truth: sqrt(2)/2 = 0.707106781186548

[deriv,err] = derivest(@(x) sin(x),pi/4,'d',4)

deriv =
      0.70711
err =
   1.9122e-05

Evaluate the indicated (default = first) derivative at multiple points

[deriv,err] = derivest(@(x) sin(x),linspace(0,2*pi,13))

deriv =
  Columns 1 through 7
            1      0.86603          0.5            0         -0.5     -0.86603           -1
  Columns 8 through 13
     -0.86603         -0.5            0          0.5      0.86603            1
err =
  Columns 1 through 7
   1.0412e-15   1.4725e-15   2.5102e-14            0   1.3754e-14   2.7429e-14   1.8034e-15
  Columns 8 through 13
   3.0284e-14   4.9044e-14            0   3.2092e-15   1.2987e-13   2.5504e-15

Specify the step size (default stepsize = 0.1)

deriv = derivest(@(x) polyval(1:5,x),1,'deriv',4,'FixedStep',1)

deriv =
           24

Provide other parameters via an anonymous function

At a minimizer of a function, its derivative should be essentially zero. So, first, find a local minima of a first kind bessel function of order nu.

nu = 0;
fun = @(t) besselj(nu,t);
fplot(fun,[0,10])
x0 = fminbnd(fun,0,10,optimset('TolX',1.e-15))
hold on
plot(x0,fun(x0),'ro')
hold off

deriv = derivest(fun,x0,'d',1)

x0 =
       3.8317
deriv =
  -2.3285e-09

The second derivative should be positive at a minimizer.

deriv = derivest(fun,x0,'d',2)

deriv =
      0.40276

Compute the numerical gradient vector of a 2-d function

Note: the gradient at this point should be [4 6]

fun = @(x,y) x.^2 + y.^2;
xy = [2 3];
gradvec = [derivest(@(x) fun(x,xy(2)),xy(1),'d',1), ...
           derivest(@(y) fun(xy(1),y),xy(2),'d',1)]

gradvec =
            4            6

Compute the numerical Laplacian function of a 2-d function

Note: The Laplacian of this function should be everywhere == 4

fun = @(x,y) x.^2 + y.^2;
xy = [2 3];
lapval = derivest(@(x) fun(x,xy(2)),xy(1),'d',2) + ...
           derivest(@(y) fun(xy(1),y),xy(2),'d',2)

lapval =
     4

Compute the derivative of a function using a central difference scheme

Sometimes you may not want your function to be evaluated above or below a given point. A 'central' difference scheme will look in both directions equally.

[deriv,err] = derivest(@(x) sinh(x),0,'Style','central')

deriv =
     1
err =
   1.0412e-15

Compute the derivative of a function using a forward difference scheme

But a forward scheme will only look above x0.

[deriv,err] = derivest(@(x) sinh(x),0,'Style','forward')

deriv =
     1
err =
   3.1516e-15

Compute the derivative of a function using a backward difference scheme

And a backward scheme will only look below x0.

[deriv,err] = derivest(@(x) sinh(x),0,'Style','backward')

deriv =
     1
err =
   3.1516e-15

Although a central rule may put some samples in the wrong places, it may still succeed

[d,e,del]=derivest(@(x) log(x),.001,'style','central')

d =
         1000
e =
   1.7072e-10
del =
   3.0518e-05

But forcing the use of a one-sided rule may be smart anyway

[d,e,del]=derivest(@(x) log(x),.001,'style','forward')

d =
         1000
e =
   6.5547e-08
del =
   0.00012207

Control the behavior of DERIVEST - forward 2nd order method, with only 1 Romberg term

Compute the first derivative, also return the final stepsize chosen

[deriv,err,fdelta] = derivest(@(x) tan(x),pi,'deriv',1,'Style','for','MethodOrder',2,'RombergTerms',1)

deriv =
            1
err =
   2.8399e-13
fdelta =
    0.0011984

Functions should be vectorized for speed, but its not always easy to do.

[deriv,err] = derivest(@(x) x.^2,0:5,'deriv',1)
[deriv,err] = derivest(@(x) x^2,0:5,'deriv',1,'vectorized','no')

deriv =
            0            2            4            6            8           10
err =
            0   4.6563e-15   9.3127e-15    1.178e-14   1.8625e-14   2.3559e-14
deriv =
            0            2            4            6            8           10
err =
            0   4.6563e-15   9.3127e-15    1.178e-14   1.8625e-14   2.3559e-14

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