Error approximation using mean value theorem for mv-function

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Homework Help Overview

The discussion centers around the application of the mean value theorem for vector functions to estimate the error in approximating a function defined as \( f(x) = \cos(x_1^2 + x_2^3) \) at specific points in \( \mathbb{R}^2 \). Participants are tasked with showing that the error of the approximation is less than \( 7 \cdot 10^{-3} \).

Discussion Character

  • Exploratory, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the differentiability of the function and the application of the mean value theorem. There are inquiries about estimating the gradient \( \nabla f(c) \) and whether to use norms or direct calculations. Some participants express confusion over the necessity of the mean value theorem and the implications of sine approximations.

Discussion Status

The discussion is ongoing, with participants exploring various approaches to estimate the error. Some have provided calculations and alternative methods, while others question the validity of certain steps and assumptions. There is no explicit consensus on the best approach yet, but several lines of reasoning are being actively examined.

Contextual Notes

Participants note the convexity of \( \mathbb{R}^2 \) and the differentiability of the function as key points. There is also mention of potential constraints regarding the values of \( c_1 \) and \( c_2 \) in the context of maximizing certain expressions, which adds complexity to the estimation process.

lep11
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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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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?
 
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 :/
 
Last edited:
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##
 
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. :(
 
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## ?
 
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?
 
Last edited:
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)##.
 

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