Trapezoidal Rule is used for approximating definite integral.

This method works by approximating the area under the function (in given interval) as trapezoid(s).

Trapezoidal Rule N = 10
Trapezoidal Rule N = 10
Trapezoidal Rule N = 20
Trapezoidal Rule N = 20

As can be seen, the bigger the n (more intervals) is, the better the integral is approximated.


Trapezoidal Rule Formula
Trapezoidal Rule Formula
Trapezoidal Rule M Formula
Trapezoidal Rule M Formula


  • a is the beggining of the interval, b is the end of the interval,
  • n is the number of steps (number of trapezoids to use),
  • f(x) is the function to integrate,
  • m is the height of i-th trapezoid.


Let’s approximate this definite integral with n = 5.

Definite Integral 0-2 x*x dx.

Trapezoidal Rule Example

Trapezoidal Rule Example 2

Trapezoidal Rule Example 3

As can be seen, for n = 5, we calculated that the deinite integral is equal to 68/25 = 2.72, which is fairly close to the actual value: 8/3 = 2.(6).

Trapezoidal Rule N Comparison

568/25 = 2.724/75 = 0.05(3)
1067/25 = 2.681/75 = 0.01(3)
251668/625 = 2.66884/1875= 0.0021(3)


Function f, which is continous function, interval [a,b] and number of iterations to perform - n.
Defined Integral approximation of given function in given interval.
1. Calculate step = (b - a) / n. It will be used as the step size of each iteration.
2. Assign 0.5 * (f(a) + f(b)) to variable integral.
3. Initialize variable i to 0.
4. Add f(a + step * i) to variable integral.
5. Increase i by one.
6. Check if i is smaller than n. If it is go to step 4.
7. Multiply integral by step.
8. End algorithm and return integral.

Sample Output:

f(x) = x * x

integral: 5.33334933334409


  • Easy to implement


  • Requires great number of iterations to be accurate, especially for big intervals (huge value of b – a)


Java Implementation R Implementation C# Implementation Python Implementation C++ Implementation



Trapezoidal Rule Algorithm

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