Error approximation using mean value theorem for mv-function

In summary: It's clear until ##|<\nabla{f(c)},(0,3;-0,3)>|\; \leq |-0,6c_1+0,9c_2^2|\cdot |\sin(c_1^2+c_2^3)|##Now ##c_1 \in [0\, , \,0.3]## and ##c_2 \in [-0.3\, , \,0]## which means the signs come into play and you lost me. We have to take the worst case here, i.e. the maximal possible value. To make it easier to look at, let's take positive values ##a:=c_1 \geq
  • #1
lep11
380
7
Let ##f:\mathbb{R^2} \rightarrow\mathbb{R}, f(x)=\cos(x_1^2+x_2^3)## and ##f(0,3;-0,3)\approx1.##Show that the error of that approximationis less than ##7\cdot10^{-3}.##

Obviously ##\mathbb{R^2}## is convex, that is, any points ##a,b\in\mathbb{R^2}## can be connected with a line segment. In addition, ##f## is differentiable as a composition of two differentiable functions. Thus, the conditions of mean value theorem for vector functions are satisfied. By applying the theorem we get

$$|\cos(0,3^2+(-0,3)^3)-1|$$
$$=|\cos(0,3^2-0,3^3)-\cos(0^2+0^3)|$$
$$=|Df(c)((0,3;-0,3)-(0,0))|$$
$$=|<\nabla{f(c)},(0,3;-0,3)>|$$
$$\leq\|\nabla{f(c)}\|\|(0,3;-0,3)\|$$(by C-S inequality)
$$=\|\nabla{f(c)}\|\sqrt{0,3^2+0,3^2},$$
$$=\sqrt{(-2c_1\sin(c_1^2+c_2^3)^2+(-3c_2^2\sin(c_1^2+c_2^3))^2}\sqrt{0,3^2+0,3^2},$$
$$\leq{\sqrt{(4c_1^2+9c_2^4}\sqrt{0,3^2+0,3^2}}$$
$$\leq...?$$

where ##c=(c_1,c_2)## lies on the line connecting ##(0,3;-0,3)## and the origin. Consequently ##\|c\|=\sqrt{c_1^2+c_2^2}\leq\sqrt{0,3^2+0,3^2}.## Further, ##\nabla{f(c)}=(-2c_1\sin(c_1^2+c_2^3),-3c_2^2\sin(c_1^2+c_2^3)).##

How could one estimate ##\|\nabla{f(c)}\|?## It is a matrix (Frobenius) norm, right?

How to proceed?
 
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  • #2
lep11 said:
Obviously ##\mathbb{R^2}## is convex, that is, any points ##a,b\in\mathbb{R^2}## can be connected with a line segment. In addition, ##f## is differentiable as a composition of two differentiable functions. Thus, the conditions of mean value theorem for vector functions are satisfied. By applying the theorem we get

$$|\cos(0,3^2+(-0,3)^3)-1|$$
$$=|\cos(0,3^2-0,3^3)-\cos(0^2+0^3)|$$
$$=|Df(c)((0,3;-0,3)-(0,0))|$$
$$=|<\nabla{f(c)},(0,3;-0,3)>|$$
$$\leq\|\nabla{f(c)}\|\|(0,3;-0,3)\|$$(by C-S inequality)
$$=\|\nabla{f(c)}\|\sqrt{0,3^2+0,3^2},$$
$$=\sqrt{(-2c_1\sin(c_1^2+c_2^3)^2+(-3c_2^2\sin(c_1^2+c_2^3))^2}\sqrt{0,3^2+0,3^2},$$
$$\leq{\sqrt{(4c_1^2+9c_2^4}\sqrt{0,3^2+0,3^2}}$$
$$\leq...?$$

where ##c=(c_1,c_2)## lies on the line connecting ##(0,3;-0,3)## and the origin. Consequently ##\|c\|=\sqrt{c_1^2+c_2^2}\leq\sqrt{0,3^2+0,3^2}.## Further, ##\nabla{f(c)}=(-2c_1\sin(c_1^2+c_2^3),-3c_2^2\sin(c_1^2+c_2^3)).##

How could one estimate ##\|\nabla{f(c)}\|?## It is a matrix (Frobenius) norm, right?

How to proceed?
Are you sure, you haven't lost too much space already? Why do you consider norms in the first place, rather than calculating ##|<\nabla{f(c)},(0,3;-0,3)>|\;##? I also think, that the approximations of the sine values should be really tight. Is the usage of the mean value theorem mandatory?
 
  • #3
fresh_42 said:
Are you sure, you haven't lost too much space already? Why do you consider norms in the first place, rather than calculating ##|<\nabla{f(c)},(0,3;-0,3)>|\;##? I also think, that the approximations of the sine values should be really tight. Is the usage of the mean value theorem mandatory?
It is mandatory to use the mean value theorem. And now

##|<\nabla{f(c)},(0,3;-0,3)>|\;=|-0,6c_1\sin(c_1^2+c_2^3)+1,8c_2^2\sin(c_1^2+c_2^3))|=|-0,6c_1+1,8c_2^2||\sin(c_1^2+c_2^3)|\leq|-0,6c_1+1,8c_2^2|...?##

or alternatively
##|<\nabla{f(c)},(0,3;-0,3)>|\;=|-0,6c_1\sin(c_1^2+c_2^3)+1,8c_2^2\sin(c_1^2+c_2^3))|\approx|-0,6c_1(c_1^2+c_2^3)+1,8c_2^2(c_1^2+c_2^3))|\leq|-0,6c_1(c_1^2+c_2^3)|+|1,8c_2^2(c_1^2+c_2^3)|...?##

Neither seems to lead me in the right direction :/
 
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  • #4
lep11 said:
It is mandatory to use the mean value theorem. And now

##|<\nabla{f(c)},(0,3;-0,3)>|\;=|-0,6c_1\sin(c_1^2+c_2^3)+1,8c_2^2\sin(c_1^2+c_2^3))|=|-0,6c_1+1,8c_2^2||\sin(c_1^2+c_2^3)|\leq|-0,6c_1+1,8c_2^2|...?##

or alternatively
##|<\nabla{f(c)},(0,3;-0,3)>|\;=|-0,6c_1\sin(c_1^2+c_2^3)+1,8c_2^2\sin(c_1^2+c_2^3))|\approx|-0,6c_1(c_1^2+c_2^3)+1,8c_2^2(c_1^2+c_2^3))|\leq|-0,6c_1(c_1^2+c_2^3)|+|1,8c_2^2(c_1^2+c_2^3|##
Shouldn't it be ##0.9## as coefficient instead of ##1.8##? And you might have to take the signs of ##c_i## into account for an upper bound of ##c_1^2+c_2^3##
 
  • #5
fresh_42 said:
Shouldn't it be ##0.9## as coefficient instead of ##1.8##? And you might have to take the signs of ##c_i## into account for an upper bound of ##c_1^2+c_2^3##
Yes it should. Btw, which one of the above is better estimation? I am still not getting the desired upper bound. :(
 
  • #6
I would step in here ##|<\nabla{f(c)},0,3;-0,3)>|\;=##
##=|-0,6c_1\sin(c_1^2+c_2^3)+0.9c_2^2\sin(c_1^2+c_2^3))|=|-0,6c_1+0.9c_2^2||\sin(c_1^2+c_2^3)|##.
With ##c_1=c_2=0.3## I get ##31 \cdot 10^{-3}##, so it's too big.
But isn't ##c_2 \leq 0##, which means ##\sin (c_1^2+c_2^3) \leq \sin c_1^2 \leq 0.3^2 = 0.09## ?
 
  • #7
fresh_42 said:
I would step in here ##|<\nabla{f(c)},0,3;-0,3)>|\;=##
##=|-0,6c_1\sin(c_1^2+c_2^3)+0.9c_2^2\sin(c_1^2+c_2^3))|=|-0,6c_1+0.9c_2^2||\sin(c_1^2+c_2^3)|##.
With ##c_1=c_2=0.3## I get ##31 \cdot 10^{-3}##, so it's too big.
But isn't ##c_2 \leq 0##, which means ##\sin (c_1^2+c_2^3) \leq \sin c_1^2 \leq 0.3^2 = 0.09## ?
##0,09\cdot|-0,6\cdot0.3+0,9\cdot0,3^2|\approx0,00891## which is too big as well.
Next I tried
##|<\nabla{f(c)},(0,3;-0,3)>|\;=|-0,6c_1\sin(c_1^2+c_2^3)+0,9c_2^2\sin(c_1^2+c_2^3))|

\leq|-0,6c_1+0,9c_2^2||\sin(c_1^2+c_2^3)|\leq|0,9c_2^2|\sin(c_1^2+c_2^3)\approx|0,9c_2^2||c_1^2+c_2^3|\approx5,1\cdot10^{-3}<7\cdot10^{-3} ##with ##c_1=0,3## and ##c_2=-0,3.## Is this correct? ##|-0,6c_1+0,9c_2^2|\leq|0,9c_2^2|?##

How can this be so hard?
 
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  • #8
lep11 said:
##0,09\cdot|-0,6\cdot0.3+0,9\cdot0,3^2|\approx0,00891## which is too big as well.
Next I tried
##|<\nabla{f(c)},(0,3;-0,3)>|\;=|-0,6c_1\sin(c_1^2+c_2^3)+0,9c_2^2\sin(c_1^2+c_2^3))|

\leq|-0,6c_1+0,9c_2^2||\sin(c_1^2+c_2^3)|\leq|0,9c_2^2|\sin(c_1^2+c_2^3)\approx|0,9c_2^2||c_1^2+c_2^3|\approx5,1\cdot10^{-3}<7\cdot10^{-3} ##with ##c_1=0,3## and ##c_2=-0,3.## Is this correct? ##|-0,6c_1+0,9c_2^2|\leq|0,9c_2^2|?##
It's clear until ##|<\nabla{f(c)},(0,3;-0,3)>|\; \leq |-0,6c_1+0,9c_2^2|\cdot |\sin(c_1^2+c_2^3)|##

Now ##c_1 \in [0\, , \,0.3]## and ##c_2 \in [-0.3\, , \,0]## which means the signs come into play and you lost me. We have to take the worst case here, i.e. the maximal possible value. To make it easier to look at, let's take positive values ##a:=c_1 \geq 0## and ##b:=-c_2 \geq 0##.
Thus we have to maximize ##m(a,b):=|-0.6 a + 0.9 b^2 \, |\,\cdot\,| \sin (a^2-b^3) \;|##.
Why should ##|-0.6 a + 0.9 b^2 \, | \leq |0.9 b^2 \, |## be the case? For ##(a,b)=(0.3\, , \,0)## this isn't true.

How can this be so hard?
Because there isn't much space to be generous without further knowledge about ##(a,b)=(c_1,-c_2)##.
 

1. What is the mean value theorem for mv-function and how is it used for error approximation?

The mean value theorem for mv-function states that for a continuous function, there exists a point in the interval where the slope of the tangent line is equal to the average rate of change of the function over that interval. This theorem is used to approximate the error between a given function and its linearization at a point.

2. Can the mean value theorem be applied to all types of functions?

No, the mean value theorem can only be applied to continuous functions. If a function is not continuous, the theorem does not hold and cannot be used for error approximation.

3. What is the equation for error approximation using the mean value theorem for mv-function?

The equation for error approximation using the mean value theorem is given by:
Error = f'(c)(x-a), where f'(c) is the derivative of the function at the point c, x is the value at which we want to approximate the function, and a is the point at which the linearization is being computed.

4. How accurate is the error approximation using the mean value theorem?

The error approximation using the mean value theorem is accurate as long as the function is continuous and differentiable in the interval. However, the accuracy may decrease as the interval becomes larger.

5. Can the mean value theorem be used for multivariable functions?

Yes, the mean value theorem can be extended to multivariable functions, known as the multivariable mean value theorem. It states that for a function of two or more variables, there exists a point in the interval where the partial derivatives of the function are equal to the average rate of change of the function over that interval.

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