### About

#
**Integral: algorithms of numerical approximation**

As an example for numerical integration we choose the sine function *y
= sin x* ; its graph is shown in blue. The **definite integral**
is to be calculated between an initial abscissa *x _{1}* and
an end abscissa

*x*.

_{2}

The **analytic solution** of the **indefinite integral**
(antiderivative) is

###
*y = ∫ sinxdx = -cos x +C *

Its graph is shown in red, with *C* as the initial value at
the initial abscissa.

The analytic definite integral is *-(cos x _{2}-cosx_{1}).
*It corresponds to a point on the analytic curve at the end abscissa

*x*.

_{2}
In the approximate numerical calculation the interval *x _{2 }-
x_{1}* is divided into

*n*sub intervals of width

*. For clear demonstration of the principle*

**delta***n = 2*is chosen. Arrows show the value of the function in the three points of the double interval

*2 delta*.

Three numerical algorithms are visualized in three windows. They differ
in how the approximative value of the function is defined between
consecutive points in the sub interval *delta.*

1.) **Rectangle approximation**: *y* is taken as constant
within the interval. The contribution of one interval is *delta * y _{1}*.

2.) **Trapezoid** approximation: y is taken as the mean value within
the interval. Its contribution is *delta*(y _{1}+y_{2})/2.*

3.) **Parabola** approximation: the function in __two__** **consecutive
intervals is approximated by a second order parabola through both end
points and the middle point of the double interval (a parabola needs
three points to be uniquely defined). The contribution of the double
integral, derived as a surprisingly simple formula, is *2*delta*1/6(y _{1}+4y_{2}+y_{3}).*

In principle one can increase the precision of the parabola algorithm still further by using higher order parabolas, with correspondingly more sub intervals of the definition range. As the second order is already very good, higher order approximations have no great practical importance. (For fun and exercise derive the formula for a third order parabola!)

The **simulation** calculates the sum of two approximating intervals
of width *delta *using the three algorithms. Their respective
values are represented by the green points.

A first slider defines the interval * delta*, a second one
the initial abscissa

**x**_{1}**,**

*defines*

**reset***delta = 1*and

*x*

_{1}= 0.5.
**E1:** Start with the default values : *x _{1 }= 0.5;
delta=1*

Compare how well the three procedures approximate the analytic solution.

**E2:** Draw the initial value with the mouse. Observe the shift of
the analytic solution, and its relation to the result of the different
algorithms. Explain mentally to some non professional what you observe!

**E4:** Reduce the interval width and observe how fast the
approximations converge to the analytic solution.

**E4: **Keep the interval small and approximately constant while
drawing the initial point. Observe whether the differences of the
algorithms are comparable for all initial points. Interpret the result!

**E5:** For special initial points the simple algorithms result in
exact agreement with the analytic value, while the parabola algorithms
shows a recognizable deviation. Does this mean that the simple ones are
better? What is the reason for identity in these cases?

### For Teachers

### Translations

### Software Requirements

Android | iOS | Windows | MacOS | |

with best with | Chrome | Chrome | Chrome | Chrome |

support full-screen? | Yes. Chrome/Opera No. Firefox/ Samsung Internet | Not yet | Yes | Yes |

cannot work on | some mobile browser that don't understand JavaScript such as..... | cannot work on Internet Explorer 9 and below |

### Credits

Dieter Roess - WEH-Foundation; Fremont Teng; Loo Kang Wee

### end faq

### Sample Learning Goals

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### For Teachers

## Integral: Algorithms of Numerical Approximation JavaScript Simulation Applet HTML5

### Instructions

#### Control Panel

#### Toggling Full Screen

#### Reset Button

Research

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### Video

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### Version:

### Other Resources

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