Det(A+B) ? det(A) + det(B)

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In summary, the statement "det(A+B) ?? det(A) + det(B)" does not hold true in both cases. Additionally, the relationship between x*det(A) and det(x*A) is not a strict inequality and can only be expressed as x^n det(A).
  • #1
phyin
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det(A+B) ?? det(A) + det(B)

I'm guessing greater than but I'm not too sure. I need a proof on this so I can be assured of it and then use the statement to prove something else.

any hint (or link to proof) would be much appreciated.

edit:

x*det(A) ?? det(x*A)
what's the relation there?
 
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  • #2


Neither holds, take

[tex]A=B=\left(\begin{array}{cc} 1 & 0\\ 0 & 1 \end{array}\right)[/tex]

for one equality and


[tex]A=\left(\begin{array}{cc} 1 & 0\\ 0 & 1 \end{array}\right),~B=-A[/tex]

for the other.

For the last question, we do have the equality

[tex]det(xA)=x^n detA[/tex]

where A is the rank of the matrix. From this we can deduce that neither of the inequalities between det(xA) and xdet(A) holds true. Just take x>1 and x<1.
 
  • #3


thanks. i better find an alternative way to my proof ;s
 
  • #4


Another simple counter-example is
[tex]A = \begin{pmatrix} a & 0 \\ 0 & 0 \end{pmatrix} \qquad
B = \begin{pmatrix} 0 & 0 \\ 0 & b \end{pmatrix}[/tex]

det(A) = det(B) = 0, det(A+B) = ab
 
  • #5


The correct relationship is actually det(A+B) = det(A) + det(B) only if A and B commute, which means that AB = BA. This is known as the distributive property of determinants.

To prove this, we can use the definition of determinants as the signed volume of a parallelepiped. Let A and B be two n x n matrices. The determinant of A+B is equal to the signed volume of the parallelepiped formed by the column vectors of A and B.

On the other hand, the determinant of A is equal to the signed volume of the parallelepiped formed by the column vectors of A, and the determinant of B is equal to the signed volume of the parallelepiped formed by the column vectors of B.

Now, when we add A and B, we are essentially combining the column vectors of A and B to form a new parallelepiped. The signed volume of this new parallelepiped will be equal to the sum of the signed volumes of the individual parallelepipeds formed by A and B.

Therefore, we can conclude that det(A+B) = det(A) + det(B) when A and B commute.

As for the relation between x*det(A) and det(x*A), we can use the same logic as above. The determinant of x*A is equal to the signed volume of the parallelepiped formed by the column vectors of x*A. This is equivalent to scaling the column vectors of A by a factor of x, which will result in a new parallelepiped with a signed volume of x*det(A). Therefore, the relationship is det(x*A) = x*det(A).
 

What is the statement "Det(A+B) = det(A) + det(B)?"

The statement "Det(A+B) = det(A) + det(B)" is a mathematical formula that shows the relationship between the determinants of two matrices, A and B, when they are added together.

What is a determinant?

A determinant is a scalar value that can be calculated from a square matrix. It represents a number that is associated with the matrix and is used in various mathematical operations, such as solving systems of linear equations.

What is the significance of the equality "Det(A+B) = det(A) + det(B)?"

The equality "Det(A+B) = det(A) + det(B)" is significant because it shows that the determinant of a sum of two matrices is equal to the sum of their individual determinants. This can be helpful in simplifying calculations involving matrices.

Can the statement "Det(A+B) = det(A) + det(B)" be applied to matrices of any size?

No, the statement "Det(A+B) = det(A) + det(B)" can only be applied to square matrices, meaning matrices with an equal number of rows and columns.

How can the statement "Det(A+B) = det(A) + det(B)" be proven?

The statement "Det(A+B) = det(A) + det(B)" can be proven using the properties of determinants, such as the distributive property and the rule that the determinant of a matrix multiplied by a scalar is equal to the determinant of the matrix multiplied by the scalar. Additionally, it can be proven using cofactor expansion and matrix multiplication.

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