What is the determinant of a block matrix?

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Discussion Overview

The discussion revolves around the determinant of a specific block matrix, denoted as \(\Sigma_{(j)}\). Participants explore the formula for the determinant, its derivation, and the challenges in understanding it. The scope includes mathematical reasoning and technical explanation related to linear algebra.

Discussion Character

  • Exploratory
  • Mathematical reasoning
  • Technical explanation

Main Points Raised

  • One participant presents the block matrix \(\Sigma_{(j)}\) and questions the derivation of its determinant formula, expressing confusion about its apparent consistency.
  • Another participant suggests that working through examples of smaller matrices could help in understanding the determinant's behavior and the underlying principles.
  • A different participant proposes a method involving left-multiplying the matrix by a specific transformation to facilitate the calculation of the determinant, referencing the determinant-of-products rule.
  • One participant concludes that the determinant can be expressed in a different form, involving the adjoint of \(\Sigma_{(2)}\), and acknowledges that their approach is more of a backward proof rather than a forward derivation.

Areas of Agreement / Disagreement

Participants do not reach a consensus on the most straightforward method for deriving the determinant. There are multiple approaches discussed, and the understanding of the formula remains somewhat unresolved.

Contextual Notes

Participants express uncertainty about the clarity and obviousness of the determinant formula. There is a reliance on specific matrix properties and operations that may not be universally understood without further exploration.

maverick280857
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Hi,

I've been trying to get my head around this. \Sigma_{(j)} is a p x p matrix given by

\Sigma_{(j)} = \left(\begin{array}{cc}\sigma_{jj} & \boldsymbol{\sigma_{(j)}'}\\\boldsymbol{\sigma_{(j)}} & \boldsymbol{\Sigma_{(2)}}\end{array}\right)

where \sigma_{jj} is a scalar, \boldsymbol{\sigma_{(j)}} is a (p-1)x1 column vector, and \boldsymbol{\Sigma_{(2)}} is a (p-1)x(p-1) matrix.

The result I can't understand is

|\Sigma_{(j)}| = |\Sigma_{(2)}|(\sigma_{jj} - \boldsymbol{\sigma_{(j)}'\Sigma_{2}^{-1}\sigma_{(j)}})

where |.| denotes the determinant. How does one get this? It seems to be consistent, but I don't 'see' how it is obvious. I searched the internet for results on determinants of block matrices but all I got was stuff for [a b;c d] where a, b, c, d are all n x n matrices, in which case the determinant is just det(ad-bc).

Any inputs would be appreciated.

Thanks in advance!
 
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Have you worked any examples? The best way to understand how something (a proof, a theorem, a process) works is to repeat it yourself. Try it for a 3x3 matrix then a 4x4 and see if you can identify the specific machinery which permits this formula.
 
Hmm, I can think of one way you could prove this, but it might not be the best or most 'obvious' way. Still, better than nothing.

Left-Multiply your matrix by

\left(\begin{array}{cc}1/\sigma_{jj} & \boldsymbol{0}\\\boldsymbol{0} & \boldsymbol{\Sigma_{(2)}^{-1}}\end{array}\right)

And see what you get. You can then work out the determinant using the determinant-of-products rule.
 
Thanks everyone who replied. It turns out that the thing is rather simple:

|\Sigma_{(j)}| = \sigma_{jj}|\Sigma_{(2)}| - \sigma'_{(j)}adj(\Sigma_{(2)})\sigma_{(j)}

(noting that the (1,2)th 'element' is actually a row, and using the usual minor-cofactor expansion of the determinant)

Then the final step involves writing the adjoint as a product of the inverse and the (scalar) determinant, which is factored out. I admit though that this is more of a backward proof, than a derivation-based forward proof.
 

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