Diagonalization of Specif Matrix

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    Diagonalization Matrix
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SUMMARY

The discussion focuses on computing the eigenvalues of a specific matrix defined as a block matrix with identity elements and additional variables. The matrix in question is structured as follows: \begin{bmatrix} 1 & 0 & 0 & \cdots & v_1 \\ 0 & 1 & 0 & \cdots & v_2 \\ 0 & 0 & 1 & \cdots & v_3 \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ v_1 & v_2 & v_3 & \cdots & v_n \end{bmatrix}. The key conclusion is that the eigenvalues can be determined by ensuring the top determinant is positive, specifically calculated as v_n - ∑(i=1 to n-1) v_i². The author expresses initial frustration but ultimately finds the solution straightforward.

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ApproxIdent
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Now this could seem like a homework problem...but it's not. (I guess you'd need to believe me or just choose not to answer my question.) I'm trying to compute the eigenvalues of a matrix and it's a little more irritating than I'd expected. All I really care is if they're positive (so all I really need is for the top determinant to be positive). The matrix is

[itex]\begin{bmatrix} 1 & 0 & 0 & \cdots & v_1 \\ 0 & 1 & 0 & \cdots & v_2 \\ 0 & 0 & 1 & \cdots & v_3 \\ \vdots & \vdots & \vdots & \ddots & \vdots \\ v_1 & v_2 & v_3 & \cdots & v_n \end{bmatrix}[/itex]

I feel like there must be some basic way of doing this that I'm missing. I'm reading a paper that works with (basically) the same matrix. The author writes down specific conditions (hich I presume is correct) so I could just accept it on face, but I'm a little frustrated at my inability to do this myself.

Anyway, if you're the type to love solving these problems you could spare me a bit of a headache...thanks for any help.
 
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Never mind...it was easy. Apparently the process of deleting rows and columns is too complex for me. In case you're curious, it's:

[itex]v_n - \sum_{i=1}^{n-1} v_i^2[/itex]
 

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