ML-inequality, Estimation of Line Integrals

In summary: Thank you so much for your patience and guidance throughout this problem. In summary, we use the fact that the magnitude of the vector function ##\vec{F}## is bounded by ##M## on the curve ##C##, and we know that the length of ##C## is ##L##. We re-express the line integral ## \int_C |\vec{F}| d\vec{r}## as ##\int_C |\vec{F}| d\vec{r} = |\vec{F}| \int_C d\vec{r}## and use the boundedness of ##|\vec{F}|## to show that ##|\int\limits_C\ \vec{F} \cdot d\vec
  • #1
wifi
115
1
Problem:

Let ##\vec{F}## be a vector function defined on a curve C. Let ##|\vec{F}|## be bounded, say, ##|\vec{F}| ≤ M## on C, where ##M## is some positive number. Show that ##|\int\limits_C\ \vec{F} \cdot d\vec{r}| ≤ ML ## (L=Length of C).Attempt at a Solution:

I honestly have no idea where to start with this one. It's not really clear to me exactly what the question is asking me to show.

I understand we have this vector function ##\vec{F}=F_1 \hat{i} + F_2 \hat{j} + F_3 \hat{k} ##, and that's about it... What does it mean to say that the magnitude ##|\vec{F}| ## is "bounded" in such a way that ##|\vec{F}| ≤ M##? Are we imposing a restriction here?
 
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  • #2
Bring the absolute values within the integral then this |F| in the integral is smaller than [tex]||F||_\infty[/tex]. What remains is a simple line integral.
 
  • #3
Is this what you're saying?

##|\int\limits_C\ \vec{F} \cdot d\vec{r}| ≤ \int\limits_C\ | \vec{F} | \cdot d\vec{r} ##
 
  • #4
Any help guys?
 
  • #5
Wouldn't ## |\vec{F}|## be a scalar? In which case taking the dot product is undefined in ##\int |\vec{F}| \cdot d\vec{r} ##?
 
  • #6
yes, the norm of [itex] {\mathbf{F}}[/itex] is a scalar, so your comment about the dot product with [itex] d {\mathbf{r}}[/itex] is correct. If the norm of [itex] {\mathbf{F}}[/itex] is a scalar, what can you do to simplify
[tex]
\int_C {\mathbf{F}} \, d{\mathbf{r}} \text{ ?}
[/tex]

and what remains when you do simplify it? (Hint: it is here where you'll use the assumption that [itex] |\mathbf{F}| \le M [/itex].)
 
  • #7
I'm not really sure how ## \int_C {\mathbf{F}} \, d{\mathbf{r}} ## can be simplified. Can you be more specific?
 
  • #8
From calculus, you can prove that for smooth ##f(x)## defined on the curve C, (which is in the domain of f(x)) then $$\left|\int f(x)\, dx \right| \leq \int \left| f(x) \right|\,dx$$
Apply this to your problem.
 
  • #9
I understand that ## \left|\int f(x)\, dx \right| \leq \int \left| f(x) \right| dx\,##, I don't see how that can be applied to this line integral.
 
  • #10
wifi said:
I understand that ## \left|\int f(x)\, dx \right| \leq \int \left| f(x) \right|\,##, I don't see how that can be applied to this line integral.

##|\underline{F}|## is a scalar. How can you reexpress ##\int |\underline{F}|\,d\underline{r}##?
 
  • #11
In the integral ##\int |\vec{F}| \cdot d\vec{r}##, isn't ## |\vec{F}| \cdot d\vec{r} ## undefined since the dot product is an operation between two vectors?
 
  • #12
wifi said:
In the integral ##\int |\vec{F}| \cdot d\vec{r}##, isn't ## |\vec{F}| \cdot d\vec{r} ## undefined since the dot product is an operation between two vectors?

It is, but the integral in my last post does not include a dot product.
If ##k## is a scalar, then ##\int k f(x) dx = ?##
Once you make this step, use the condition of the boundedness of f.
 
  • #13
## \int k f(x) dx = k\int f(x)dx ##. So I think you're trying to say ## \int |\vec{F}| d\vec{r} = |\vec{F}| \int d\vec{r}##. Right?
 
  • #14
wifi said:
## \int k f(x) dx = k\int f(x)dx ##. So I think you're trying to say ## \int |\vec{F}| d\vec{r} = |\vec{F}| \int d\vec{r}##. Right?

Right. Now use the boundedness of ##|\underline{F}|## and evaluate ##\int_C d \underline{r}##
 
  • #15
So since we know that ##|\vec{F}| \leq M## and ##\int d\vec{r}=L## , we have ## \int |\vec{F}| d\vec{r} = |\vec{F}| \int d\vec{r} \leq M \int d\vec{r} = ML##. Yes?
 
  • #16
wifi said:
So since we know that ##|\vec{F}| \leq M## and ##\int d\vec{r}=L##, we have ## \int |\vec{F}| d\vec{r} = |\vec{F}| \int d\vec{r} \leq M \int d\vec{r} = ML##. Yes?

Yes, you are done.
 
  • #17
CAF123, you're the best!
 

What is ML-inequality and how is it used in estimation of line integrals?

ML-inequality, also known as the Cauchy-Schwarz inequality, is a mathematical inequality that is used to estimate the value of a line integral. It states that the absolute value of the dot product of two vectors is always less than or equal to the product of the magnitudes of the two vectors. This is useful in estimation of line integrals as it allows us to bound the value of the integral and make approximations.

What is the process for estimating line integrals using ML-inequality?

The process for estimating line integrals using ML-inequality involves breaking down the curve into smaller line segments and approximating the value of the integral for each segment. Then, using ML-inequality, we can bound the value of the integral for each segment and add them together to get an estimate for the overall line integral.

How does the choice of partition affect the accuracy of the estimation of line integrals using ML-inequality?

The choice of partition, or how we break down the curve into smaller line segments, can greatly affect the accuracy of the estimation of line integrals using ML-inequality. A finer partition (smaller line segments) will result in a more accurate estimation, while a coarser partition (larger line segments) may result in a less accurate estimation.

What are some limitations of using ML-inequality in estimation of line integrals?

One limitation of using ML-inequality is that it can only provide an upper bound for the value of the line integral. This means that the actual value of the integral may be lower than the estimated value. Additionally, ML-inequality may not be applicable in certain cases where the curve is very complex or the integrand is not bounded.

Are there any other methods for estimating line integrals besides ML-inequality?

Yes, there are other methods for estimating line integrals such as the trapezoidal rule, Simpson's rule, and the midpoint rule. These methods may be more accurate in certain situations and do not have the limitation of providing only an upper bound for the value of the integral. However, ML-inequality is a useful tool for quick estimations and can be easily applied in many cases.

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