Using Upper, Lower Sum, Prove the Following

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In summary, the homework statement is that F(x)=x^{2} from [a,b]. To find M and m, we use the maximum and minimum points on the interval [a,b] and divide it into n pieces. M_i=t^{2}_{i-1}; M_{i}=t^{2}_{i}.
  • #1
Astrum
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Homework Statement


[tex]f(x)=x^{2}[/tex] from [a,b]

Prove that [tex]F(x)=\frac{b^{3}}{2}-\frac{a^{3}}{2}[/tex]

Homework Equations


The Attempt at a Solution


Using the definition of an integral, we get:
[tex]U(f,P)=\sum^{n}_{i=1}M_{i}(t_{i}-t_{i-1})[/tex]
[tex]L(f,P)\sum^{n}_{i=1}m_{i}(t_{i}-t_{i-1})[/tex]

for the function x2, how do we find M?

I'm somewhat confused, a nudge in the right directions is welcomed.
 
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  • #2
Divide the interval [a,b] into n pieces and let t be the boundaries of these pieces; so t0 = a, t1 = a + (b - a) / n, t2 = a + 2 (b - a) / n, etc.
Mi is the maximum of f on the piece [ti-1, ti] and mi is the minimum.

It may become clearer if you draw a picture in which you draw the graph, the division of [a, b] and the rectangular areas that make up U(f, P) or L(f, P)
 
  • #3
CompuChip said:
Divide the interval [a,b] into n pieces and let t be the boundaries of these pieces; so t0 = a, t1 = a + (b - a) / n, t2 = a + 2 (b - a) / n, etc.
Mi is the maximum of f on the piece [ti-1, ti] and mi is the minimum.

It may become clearer if you draw a picture in which you draw the graph, the division of [a, b] and the rectangular areas that make up U(f, P) or L(f, P)

Ah, that's right, the partition is a=t0<t1<...<tn=b

So we get this:
[tex]\sum^{n}_{i=1}M_{i}(a+\frac{b-a}{n})[/tex]

But I still am not understanding where we get M and m from. We need to find the maximum minimum points on the interval? Or is there an easier way?
 
  • #4
As long as a and b are positive, [itex]x^2[/itex] is an increasing function so the maximum value on each interval, [itex]M_i[/itex], is at the right end and the minimum value on each interval, [itex]m_i[/itex], is at the left end. Further, if you divide [a, b] into n intervals, each interval has length [itex]t_i- t_{i-1}= (b- a)/n[/itex], the left endpoint is a+ (b- a)i/n for i= 0 to n-1, and the right endpoint is a+ (b- a)i/n for i= 1 to n. That is, [itex]M_i= (a+ (b-a)i/n)^2[/itex], for i= 1 to n and [itex]m_i= (a+ (b-a)i/n)^2[/itex] for i= 0 to n-1.
[itex]M_i(t_i- t_{i-1})= ((b-a)/n)(a+ (b-a)i/n)^2[/itex] for i= 1 to n and [itex]m_i(t_i- t_{i-1})= ((b- a)/n)(a+ (b- a)i/n)^2[/itex]
Multiply those out and sum. It will help if you know that [itex]\sum_{i=1}^n c= nc[/itex], [itex]\sum_{i=1}^n i= n(n+ 1)/2[/itex] and [itex]\sum_{i=1}^n i^2= [n(n+1)(2n+1)]/6[/itex]. Those should be in your text.
 
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  • #5
HallsofIvy said:
As long as a and b are positive, [itex]x^2[/itex] is an increasing function so the maximum value on each interval, [itex]M_i[/itex], is at the right end and the minimum value on each interval, [itex]m_i[/itex], is at the left end. Further, if you divide [a, b] into n intervals, each interval has length [itex]t_i- t_{i-1}= (b- a)/n[/itex], the left endpoint is a+ (b- a)i/n for i= 0 to n-1, and the right endpoint is a+ (b- a)i/n for i= 1 to n. That is, [itex]M_i= (a+ (b-a)i/n)^2[/itex], for i= 1 to n and [itex]m_i= (a+ (b-a)i/n)^2[/itex] for i= 0 to n-1.
[itex]M_i(t_i- t_{i-1})= ((b-a)/n)(a+ (b-a)i/n)^2[/itex] for i= 1 to n and [itex]m_i(t_i- t_{i-1})= ((b- a)/n)(a+ (b- a)i/n)^2[/itex]
Multiply those out and sum. It will help if you know that [itex]\sum_{i=1}^n c= nc[/itex], [itex]\sum_{i=1}^n i= n(n+ 1)/2[/itex] and [itex]\sum_{i=1}^n i^2= [n(n+1)(2n+1)]/6[/itex]. Those should be in your text.

You had some formatting problems XD

I think I get what you're saying though, it makes sense. So, in the interval [a,b], the highest value of f(x) will be at b. If I understand what you're saying, M will be equal to ti2, and m will be t2i-1?

There seems to be some weird latex problem.
 
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  • #6
I think I got it.

[tex]U(f,P)=\sum^{n}_{i=1}M_{i}(t_{i}-t_{i-1})[/tex]
[tex]L(f,P)=\sum^{n}_{i=1}m{i-1}(t_{i}-t_{i-1})[/tex]

[itex]m_{i}=t^{2}_{i-1}; M_{i}=t^{2}_{i}[/itex]

[tex]=\sum^{n}_{i=1}t^{2}_{i}(\frac{b-a}{n})[/tex]
[tex]=\sum^{n}_{i=1}t^{2}_{i-1}(\frac{b-a}{n})[/tex]

[tex]t^{2}_{i}=i^{2}\frac{b^{2}-a^{2}}{n^{2}}[/tex]
[tex]t^{2}_{i-1}=(i-1)^{2}\frac{b^{2}-a^{2}}{n^{2}}[/tex]

[tex]=\sum^{n}_{i=1}i^{2}\frac{b^{2}-a^{2}}{n^{2}}(\frac{b-a}{n})[/tex]
[tex]=\sum^{n}_{i=1}(i-1)^{2}\frac{b^{2}-a^{2}}{n^{2}}(\frac{b-a}{n})[/tex]

Now we just need to simplify, right?
 
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FAQ: Using Upper, Lower Sum, Prove the Following

1. What is the purpose of using upper and lower sums?

The purpose of using upper and lower sums is to approximate the area under a curve or the value of a definite integral. By dividing the area into smaller rectangles and calculating their sum, we can get closer and closer to the exact value of the integral.

2. How do you calculate the upper and lower sums for a function?

To calculate the upper sum, we divide the interval into smaller subintervals and take the maximum value of the function on each subinterval. Then, we multiply this maximum value by the width of the subinterval and add all of these values together. To calculate the lower sum, we use the minimum value of the function on each subinterval instead.

3. What does it mean to "prove" a statement using upper and lower sums?

To "prove" a statement using upper and lower sums means to show that the upper and lower sums of a function converge to the same value as the subintervals get smaller and smaller. This proves that the function is Riemann integrable and the value of the integral is equal to this common limit.

4. Can upper and lower sums be used for any type of function?

Yes, upper and lower sums can be used for any function that is Riemann integrable. This means that the function is bounded and has a finite number of discontinuities on the given interval.

5. How do you determine the number of subintervals to use for calculating upper and lower sums?

The number of subintervals is usually determined by the desired level of precision. The more subintervals used, the more accurate the approximation will be. However, too many subintervals can be computationally expensive, so a balance must be struck between accuracy and efficiency.

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