Cauchy expansion of determinant of a bordered matrix

In summary, the Cauchy expansion provides a formula for the determinant of a matrix with a scalar and two vectors as its last row and column. This formula involves the adjugate matrix of an n-1 by n-1 submatrix and a scalar multiple of its determinant. It is proven in Matrix Analysis by Horn and Johnson, assuming that the submatrix A is a principal submatrix. However, it holds for any submatrix A, as all such matrices are related by simple permutations. Thus, the same formula applies when the submatrix A is in the top left corner.
  • #1
ekkilop
29
0
The Cauchy expansion says that

[itex] \text{det} \begin{bmatrix}
A & x \\[0.3em]
y^T & a
\end{bmatrix}
= a \text{det}(A) - y^T \text{adj}(A) x [/itex],

where A is an n-1 by n-1 matrix, y and x are vectors with n-1 elements, and a is a scalar.
There is a proof in Matrix Analysis by Horn and Johnson that seems to be based on that A is a principal submatrix. My question is whether some similar result holds if A is not a principal submatrix? Say that we look for

det[itex] \begin{bmatrix}
y^T & a \\[0.3em]
A & x
\end{bmatrix}
[/itex].

Would a similar expression hold?

Thanks.
 
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  • #2
ekkilop said:
The Cauchy expansion says that

[itex] \text{det} \begin{bmatrix}
A & x \\[0.3em]
y^T & a
\end{bmatrix}
= a \text{det}(A) - y^T \text{adj}(A) x [/itex],

where A is an n-1 by n-1 matrix, y and x are vectors with n-1 elements, and a is a scalar.
There is a proof in Matrix Analysis by Horn and Johnson that seems to be based on that A is a principal submatrix. My question is whether some similar result holds if A is not a principal submatrix? Say that we look for

det[itex] \begin{bmatrix}
y^T & a \\[0.3em]
A & x
\end{bmatrix}
[/itex].

Would a similar expression hold?

Thanks.
Indeed. In fact, it would just be ##\vec{y}^T \operatorname{adj}\textbf{A} \vec{x} - a\operatorname{det}\textbf{A}##. Can you see why? :tongue:
 
  • #3
Hi!

It just dawned on me that any such matrices (I suppose there are only 4 places A could go ^^, ) are related by simple permutations. Since any permutation matrix has determinant + or - 1 then what you say must be true.

Thank you for the enlightenment! =)
 

1. What is a Cauchy expansion of a determinant of a bordered matrix?

The Cauchy expansion of a determinant of a bordered matrix is a formula used to calculate the determinant of a matrix, which is a numerical value that represents the scaling factor of the transformation represented by the matrix. It involves expanding the determinant into a sum of products of elements from the matrix, with alternating signs.

2. How is a Cauchy expansion different from other methods of calculating determinants?

A Cauchy expansion is a specific formula that can be used to calculate the determinant of a bordered matrix, while other methods such as cofactor expansion or row reduction can be used for any type of matrix. Additionally, a Cauchy expansion involves expanding the determinant into a sum of products, while other methods may involve different operations such as multiplying by constants or adding rows/columns.

3. When is a Cauchy expansion of a determinant of a bordered matrix useful?

A Cauchy expansion can be useful when the matrix is large and has a specific structure, such as being a bordered matrix. In these cases, the Cauchy expansion formula can be simpler and more efficient to use compared to other methods of calculating determinants.

4. Can a Cauchy expansion be used for non-square matrices?

No, a Cauchy expansion can only be used for square matrices, as the determinant is only defined for square matrices. Non-square matrices do not have a determinant, but they may have other properties that can be calculated using other methods.

5. Are there any applications of Cauchy expansion of determinants in real-world problems?

Yes, Cauchy expansion and determinants in general have many applications in various fields such as physics, economics, and engineering. They can be used to solve systems of linear equations, calculate volumes and areas, and analyze transformations in areas such as signal processing and computer graphics.

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