Jtdylktuy

How do I prove that the norm of a matrix equals the absolutely largest eigenvalue of the matrix? This is the precise question:

Let $A$ be a symmetric $n times n$ matrix. Consider $A$ as an operator in $mathbb{R}^n$ given by $x mapsto Ax$. Prove that $|A| = max_j |lambda_j|$, where $lambda_j$ are the eigenvalues of $A$.

I've read the relevant sections in my literature over and over but can't find any clue on how to begin. A solution is suggested here but the notion of diagonal operator is not in my literature so it doesn't tell me very much. So, any other hints on how to solve the question? Thanks.

The norm of a matrix is defined as
begin{equation}
|A| = sup_{|u| = 1} |Au|
end{equation}
Taking the singular value decomposition of the matrix $A$, we have
begin{equation}
A = VD W^T
end{equation}
where $V$ and $W$ are orthonormal and $D$ is a diagonal matrix. Since $V$ and $W$ are orthonormal, we have $|V| = 1$ and $|W| = 1$. Then $|Av| = |D v|$ for any vector $v$. Then we can maximize the norm of $Av$ by maximizing the norm of $Dv$.

By the definition of singular value decomposition, $D$ will have the singular values of $A$ on its main diagonal and will have zeros everywhere else. Let $lambda_1, ldots, lambda_n$ denote these diagonal entries so that

begin{equation}
D = left(begin{array}{cccc}
lambda_1 & 0 & ldots & 0 \
0 & lambda_2 & ldots & 0 \
vdots & & ddots & vdots \
0 & 0 & ldots & lambda_n
end{array}right)
end{equation}

Taking some $v = (v_1, v_2, ldots, v_n)^T$, the product $Dv$ takes the form
begin{equation}
Dv = left(begin{array}{c}
lambda_1v_1 \
vdots \
lambda_nv_n
end{array}right)
end{equation}
Maximizing the norm of this is the same as maximizing the norm squared. Then we are trying to maximize the sum
begin{equation}
S = sum_{i=1}^{n} lambda_i^2v_i^2
end{equation}
under the constraint that $v$ is a unit vector (i.e., $sum_i v_i^2 = 1$). The maximum is attained by finding the largest $lambda_i^2$ and setting its corresponding $v_i$ to $1$ and then setting each other $v_j$ to $0$. Then the maximum of $S$ (which is the norm squared) is the square of the absolutely largest eigenvalue of $A$. Taking the square root, we get the absolutely largest eigenvalue of $A$.

But the key point is exactly that your matrix is diagonalizable more specially that you can find an orthonormal basis of eigenvector $e_i$ for which you have $A e_i=lambda_i e_i$.

Then your write for $x=sum x_ie_i$ and you have $Ax=sum x_ilambda_i e_i$ so that $|Ax|^2=sumlambda_i^2x_i^2$ now by definition of the norm of matrix it gives you $$frac{|Ax|}{|x|}leq|lambda_{i_0}|$$ therefore $|A|leq|lambda_{i_0}|$ where $lambda_{i_0}$ is the greatest eigenvalue . Finally the identity $|Ae_{i_0}|=|lambda_{i_0}e_i|$ gives you the inequality $|A|geq|lambda_{i_0}|$.