Finding the 2-norm of a 2x2 matrix

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Homework Help Overview

The discussion revolves around finding the 2-norm of a 2x2 matrix, specifically the matrix \(\begin{pmatrix} 4 & 2 \\ 2 & 1 \end{pmatrix}\). Participants explore different methods and reasoning related to matrix norms.

Discussion Character

  • Exploratory, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • The original poster attempts to use Lagrange multipliers to find the matrix norm but expresses uncertainty about the next steps after deriving partial derivatives. They also question the validity of their values for \(\mu\) and the associated unit vectors.
  • Another participant suggests that the method being used is correct but indicates that there may be an error in the final steps.
  • A different participant reflects on a contrasting example with a 3x3 matrix and questions why a straightforward method of summing squares does not yield the expected norm.

Discussion Status

The discussion is ongoing, with participants sharing their attempts and uncertainties. Some guidance is offered regarding the method, but no consensus or resolution has been reached. Multiple interpretations of the problem and methods are being explored.

Contextual Notes

Participants are working under the constraints of homework rules, which may limit the information they can share or the methods they can use. There are indications of confusion regarding the application of matrix norms and the validity of certain approaches.

TheFerruccio
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Homework Statement


Find the matrix norm.

Homework Equations



\left(<br /> \begin{array}{cc}<br /> 4 &amp; 2 \\<br /> 2 &amp; 1 \\<br /> \end{array}<br /> \right)

The Attempt at a Solution



There are definitely different ways to solve this. I will use Lagrange multipliers.

\textbf{A}x =<br /> \left(<br /> \begin{array}{cc}<br /> 4x_1+2x_2 \\<br /> 2x_1+x_2 \\<br /> \end{array}<br /> \right)

(\left|\textbf{A}\textbf{x}\right|)^2=(4x_1+2x_2)(4x_1+2x_2)+(2x_1+x_2)(2x_1+x_2)=18x_1^2+20x_1 x_2+5x_2^2

Use Lagrange multipliers at this step, with the condition that the norm of the vector we are using is x. The goal is to find the unit vector such that A maximizes its scaling factor.

f = 18x^2+20x_1x_2+5x_2^2+\mu(x_1^2+x_2^2)

Find the derivatives in the ::x_1:: and ::x_2:: directions and set each to 0.

\frac{\partial f}{\partial x_1}=36x_1+20x_2+2\mu x_1=0
\frac{\partial f}{\partial x_2}=20x_1+10x_2+2\mu x_2=0

I am not sure where to go from here. I know that the norm of the matrix is 5, and I tried comparing coefficients to get different values for ::\mu:: and pick the right ::\mu:: for the right unit vector. If I were to directly compare coefficients, I get two different values for ::\mu::.
First value:
\mu = -18
Second value:
\mu = -5

These are associated with two unit vectors which would make the the two partial derivative equations hold true:
(1,0)
and
(0,1)

These are definitely wrong, because if I use the first unit vector (1,0), the associated magnitude of Ax would be root 18. If I were to use the second unit vector (0,1), the associated magnitude would be root 5.

What am I doing wrong?
 
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I know this has to be the right method, but the last step that I am taking is wrong. Does anyone have any insight into what is going on? I'm wasting hours on this thing.
 
I guess that's a no.

I take it I can just add up the squares of the indices, take the square root of the whole thing, and get the answer for the norm, which is 5.

But, why doesn't this work for the following matrix?

\left(<br /> \begin{array}{ccc}<br /> -1 &amp; 0 &amp; 0 \\<br /> 0 &amp; 2 &amp; 0 \\<br /> 0 &amp; 0 &amp; 4 \\<br /> \end{array}<br /> \right)

The norm of this matrix is 4, but, when I add the squares, I get \sqrt{21}.
 
I guess my question's not that interesting. Oh well. The test is in a few minutes. Too late now.
 

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