(Numerical Differentiation) Suppose we are given a function f(x) whose drivative is not known explicitly. We may, however, wish to still approximate the value of the derivative of f at a fixed point. In order to do this, we could use the so-called second- order centered difference formula given by S(r+h) – S(x - h) S'(1) = 2h Other approximations exist and are studied in numerical analysis (MAT 4020). diff_cdf Function: Input variables: • an anonymous function representing S • a scalar representing the location r where the derivative is desired • a scalar representing the value of h to be used in the approximation Output variables: • a scalar representing the approximate value of f'(x) computed using the above formula A possible sample case is: > df = diff_cdf(@(x) sin(x), 0, 0.1) df = 0.99833

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(Numerical Differentiation) Suppose we are given a function f(x) whose drivative is
not known explicitly. We may, however, wish to still approximate the value of the
derivative of f at a fixed point. In order to do this, we could use the so-called second-
order centered difference formula given by
S(r+h) – S(x - h)
S'(1) =
2h
Other approximations exist and are studied in numerical analysis (MAT 4020).
diff_cdf Function:
Input variables:
• an anonymous function representing S
• a scalar representing the location r where the derivative is desired
• a scalar representing the value of h to be used in the approximation
Output variables:
• a scalar representing the approximate value of f'(x) computed using the
above formula
A possible sample case is:
> df = diff_cdf(@(x) sin(x), 0, 0.1)
df =
0.99833
Transcribed Image Text:(Numerical Differentiation) Suppose we are given a function f(x) whose drivative is not known explicitly. We may, however, wish to still approximate the value of the derivative of f at a fixed point. In order to do this, we could use the so-called second- order centered difference formula given by S(r+h) – S(x - h) S'(1) = 2h Other approximations exist and are studied in numerical analysis (MAT 4020). diff_cdf Function: Input variables: • an anonymous function representing S • a scalar representing the location r where the derivative is desired • a scalar representing the value of h to be used in the approximation Output variables: • a scalar representing the approximate value of f'(x) computed using the above formula A possible sample case is: > df = diff_cdf(@(x) sin(x), 0, 0.1) df = 0.99833
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