Approximating discrete sum by integral

In summary, the conversation discusses the approximation of the Riemann Sum and how it works by using a moderate value of m and drawing the graph of a continuous function. The sum is represented by the area under the rectangles and is roughly equal to the integral of the function. Geogebra can be used to visualize this concept.
  • #1
Kashmir
468
74
I can't understand how this approximation works ##\sum_{k=0}^m\left(\frac{k}{m}\right)^n\approx\int_0^m\left(\frac{x}{m}\right)^ndx\tag{1}##Can you please help me
 
Physics news on Phys.org
  • #2
Choose a moderate value of m, not too small, say m=20.
Then set n=1, and draw the graph of ##f(x) = (\frac xm)^n## between 0 and ##m##. On the same set of axes draw the series of m rectangles such that the k-th rectangle (k = 0, ..., m-1) is the set of points (x,y) with ##\frac km\le x\le \frac{k+1}m## and ##(\frac xm)^n\le y\le (\frac{x+1}m)^n##.

Then do the same thing on a new set of axes, for ##n=2##.

That should give you a strong sense for why the approximation works.
 
  • Like
Likes WWGD and Kashmir
  • #4
andrewkirk said:
Choose a moderate value of m, not too small, say m=20.
Then set n=1, and draw the graph of ##f(x) = (\frac xm)^n## between 0 and ##m##. On the same set of axes draw the series of m rectangles such that the k-th rectangle (k = 0, ..., m-1) is the set of points (x,y) with ##\frac km\le x\le \frac{k+1}m## and ##(\frac xm)^n\le y\le (\frac{x+1}m)^n##.

Then do the same thing on a new set of axes, for ##n=2##.

That should give you a strong sense for why the approximation works.
We first think about the sum as the area under the rectangles having unit width.
This area is roughly the integral of the corresponding continuous function.

Am i right?
 
  • #6
Kashmir said:
We first think about the sum as the area under the rectangles having unit width.
This area is roughly the integral of the corresponding continuous function.

Am i right?
Yes, that's correct. It is called a Riemann Sum. You can read more about them here, including some nice pictures.
 
  • Informative
  • Like
Likes Kashmir and WWGD
  • #7
andrewkirk said:
Yes, that's correct. It is called a Riemann Sum. You can read more about them here, including some nice pictures.
Thank you :)
 

FAQ: Approximating discrete sum by integral

What is the purpose of approximating a discrete sum by an integral?

The purpose of approximating a discrete sum by an integral is to find an easier way to calculate the sum of a large number of discrete data points. By using the integral, we can represent the sum as a continuous function, making it easier to manipulate and analyze.

How does one approximate a discrete sum by an integral?

To approximate a discrete sum by an integral, we first divide the interval of the sum into smaller intervals. Then, we use the midpoint or trapezoidal rule to estimate the area under the curve of each interval. Finally, we add up all of these estimated areas to get an approximation of the original discrete sum.

What is the difference between a discrete sum and an integral?

A discrete sum is a finite sum of individual data points, while an integral is a continuous function that represents the sum of an infinite number of data points. In other words, a discrete sum is a collection of individual values, while an integral is a representation of the sum as a whole.

When is it appropriate to use an approximation of a discrete sum by an integral?

It is appropriate to use an approximation of a discrete sum by an integral when the number of data points is large and the function representing the data is smooth and continuous. This method is also useful when the exact value of the sum is difficult to calculate or unknown.

What are the limitations of approximating a discrete sum by an integral?

One limitation of approximating a discrete sum by an integral is that it can only provide an estimation of the original sum, not the exact value. Additionally, this method may not be accurate if the function representing the data is not smooth or if the number of data points is too small. It is also important to note that this method is only applicable for one-dimensional data and cannot be used for multi-dimensional data.

Similar threads

Replies
4
Views
2K
Replies
1
Views
2K
Replies
8
Views
1K
Replies
16
Views
3K
Replies
3
Views
968
Replies
5
Views
2K
Replies
3
Views
943
Back
Top