What is the determinant of a block matrix?

In summary, the conversation discusses the formula for the determinant of a block matrix, \Sigma_{(j)}, and how to obtain it. The formula is given by |\Sigma_{(j)}| = |\Sigma_{(2)}|(\sigma_{jj} - \boldsymbol{\sigma_{(j)}'\Sigma_{2}^{-1}\sigma_{(j)}}), where |.| denotes the determinant. The conversation suggests working out an example to better understand the formula and mentions a potential proof method involving left-multiplying the matrix by a certain matrix. Ultimately, the formula is found through a backward proof and involves writing the adjoint as a product of the inverse and the determinant.
  • #1
maverick280857
1,789
4
Hi,

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

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

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

The result I can't understand is

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

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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  • #2
Someone?
 
  • #3
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.
 
  • #4
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

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

And see what you get. You can then work out the determinant using the determinant-of-products rule.
 
  • #5
Thanks everyone who replied. It turns out that the thing is rather simple:

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

(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.
 

What is a block matrix and how is it different from a regular matrix?

A block matrix is a matrix that is made up of smaller matrices, known as blocks, arranged in a specific pattern. These blocks are separated by a horizontal or vertical line. Unlike a regular matrix, a block matrix can have different sized blocks within it.

What is the determinant of a block matrix?

The determinant of a block matrix is a single number that is calculated using the determinants of the individual blocks within the matrix. It represents the scaling factor of the matrix and can be used to determine if the matrix is invertible.

How is the determinant of a block matrix calculated?

To calculate the determinant of a block matrix, you first find the determinants of each individual block. Then, these determinants are combined using the block matrix formula, which involves multiplying the individual determinants and taking the sum or difference of these products.

How can the determinant of a block matrix be used in real-world applications?

The determinant of a block matrix has various applications in fields such as engineering, physics, and economics. It can be used to analyze the stability of a system, determine the solution to a system of linear equations, and calculate the area or volume of a shape in three-dimensional space.

Are there any special properties or rules that apply to the determinant of a block matrix?

Yes, there are several properties and rules that apply to the determinant of a block matrix. These include the linearity property, where the determinant of a matrix can be expressed as a sum or difference of determinants of smaller matrices, and the block multiplication rule, where the determinant of a block matrix can be calculated by multiplying the determinants of each block in a specific order. Additionally, the determinant of a block diagonal matrix is equal to the product of the determinants of the diagonal blocks.

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