How Accurate Are the Bounds for Eigenvalues in Circulant Matrices?

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EngWiPy
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Hi,

I have the following equation:

[tex]\gamma=\frac{1}{\frac{1}{N}\sum_{n=1}^N|\lambda_n|^{-2}}[/tex]

where lambdas are the eigenvalues of an N-by-N circulant matrix A.

I used two properties to bound the above equation:

[tex]\frac{1}{N}\sum_{n=1}^N|\lambda_n|^{-2}\geq\left(\prod_{n=1}^N|\lambda_n|^{-2}\right)^{1/N}[/tex]

[tex]\sum_{n=1}^N|\lambda_n|^{-2}\leq\left(\sum_{n=1}^N|\lambda_n|^{2}\right)^{-1}[/tex]

Are these two bounds correct?

Thanks
 
on Phys.org
The first one is a a statement that the arithmetic mean bounds the geometric mean (true).

The second is false - simple example: 2 terms on the left 1 + 2 = 3. Right side is 1/(1+1/2) = 2/3.
 
mathman said:
The first one is a a statement that the arithmetic mean bounds the geometric mean (true).

The second is false - simple example: 2 terms on the left 1 + 2 = 3. Right side is 1/(1+1/2) = 2/3.

But we have the identity for the trace that:

[tex]\sum_{n=1}^N\lambda_n^k=\text{Tr}\left(\mathbf{A}^k\right)\leq\left[\text{Tr}\left(\mathbf{A}\right)\right]^k=\left(\sum_{n=1}^N\lambda_n\right)^k[/tex]

or it just works for k>=1?