Proof on det(AB) = det(A)*det(B)

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In summary: Which means the permutation of the bracket that corresponds to the permutation of Q(k) (i.e. the term of that bracket with the same permutation of columns as Q(k) ) will have the same sign as Q(k) because the sign of Q(k) is dependent on the permutation of that bracket. Therefore the product of the b's in that bracket will cancel out, giving us the same determinant for each bracket, which is the determinant of B. In summary, the columns of matrix AB can be expressed as linear combinations of the columns of matrix A, and by result of problem 14, rearranging the columns of AB does not change the absolute value of the determinant. This allows us to separate the columns of AB into the vectors
  • #1
unscientific
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Homework Statement



Let A and B be nxn matrices, show that det(A)=det(A)det(B).

determinant_product.png




Homework Equations





The Attempt at a Solution



determinant_product2.png


I'm not sure how to express each column as a linear combination of vectors purely from A, or even from B. It's more of the dot product form: aT°b
 
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  • #3
I think you should tell us what your "general definition of the determinant" is. It's probably easiest to go directly from there.
 
  • #4
Dick said:
I think you should tell us what your "general definition of the determinant" is. It's probably easiest to go directly from there.

But i think we should first express the columns of matrix AB in terms of the columns of matrix A? (I'm not sure where B comes in) then somehow show that it is a linear combination, then make use of result 13. Then I think result 14 comes in when you get something that is just jumbled up, which simply gives det (AB) = sgn(P)*det(A)*det(B)

det(A) = Ʃ sgn(P) AP(1)1AP(2)2...
 
  • #5
bumpp
 
  • #6
As you noted, the first column of AB is
$$\begin{pmatrix}
\sum_{k=1}^n a_{1k}b_{k1} \\
\sum_{k=1}^n a_{2k}b_{k1} \\
\vdots \\
\sum_{k=1}^n a_{nk}b_{k1}
\end{pmatrix} =
\begin{pmatrix}
a_{11}b_{11} + a_{12}b_{21} + a_{13}b_{31} + \cdots + a_{1n}b_{n1} \\
a_{21}b_{11} + a_{22}b_{21} + a_{23}b_{31} + \cdots + a_{2n}b_{n1} \\
\vdots \\
a_{n1}b_{11} + a_{n2}b_{21} + a_{n3}b_{31} + \cdots + a_{nn}b_{n1}
\end{pmatrix}.$$ Express that in the form ##c_1\vec{a}_1 + c_2\vec{a}_2 + \cdots + c_n\vec{a}_n## where ##\vec{a}_k## is the kth column of A. How are the ##c_i##'s related to the elements of B?
 
  • #7
vela said:
As you noted, the first column of AB is
$$\begin{pmatrix}
\sum_{k=1}^n a_{1k}b_{k1} \\
\sum_{k=1}^n a_{2k}b_{k1} \\
\vdots \\
\sum_{k=1}^n a_{nk}b_{k1}
\end{pmatrix} =
\begin{pmatrix}
a_{11}b_{11} + a_{12}b_{21} + a_{13}b_{31} + \cdots + a_{1n}b_{n1} \\
a_{21}b_{11} + a_{22}b_{21} + a_{23}b_{31} + \cdots + a_{2n}b_{n1} \\
\vdots \\
a_{n1}b_{11} + a_{n2}b_{21} + a_{n3}b_{31} + \cdots + a_{nn}b_{n1}
\end{pmatrix}.$$ Express that in the form ##c_1\vec{a}_1 + c_2\vec{a}_2 + \cdots + c_n\vec{a}_n## where ##\vec{a}_k## is the kth column of A. How are the ##c_i##'s related to the elements of B?

So, the first column of AB = b11##\vec{a}_1## + b21##\vec{a}_2## + ... + bn1##\vec{a}_n##

kth-column = b1k##\vec{a}_1## + b2k##\vec{a}_2## + ... + bnk##\vec{a}_n##

I'm just not used to the idea of having "columns within a column", it sort of makes it more than nxn-dimensional.

Also, the earlier problem of

det(A) = λdet(B) + μdet(C)

only had one "special column" in matrix B and C that had a coefficient, while the rest were simply ##\vec{a}_1, \vec{a}_2, ... \vec{a}_n##
 
Last edited:
  • #8
Good! So you've done what the first part of the hint suggested, expressing the columns of AB as linear combinations of the columns of A. Now just follow through with the rest of the hint.
 
  • #9
vela said:
Good! So you've done what the first part of the hint suggested, expressing the columns of AB as linear combinations of the columns of A. Now just follow through with the rest of the hint.

Also, the earlier problem of

det(A) = λdet(B) + μdet(C)

only had one "special column" in matrix B and C that had a coefficient, while the rest were simply ##\vec{a}_1, \vec{a}_2, ... \vec{a}_n##

while in the new form, it's literally in every column.
 
  • #10
I think I got it:

We can simply rearrange the columns of matrix AB so as to separate them into the vectors ##\vec{a}_1, \vec{a}_2, \vec{a}_3 ...## and by result of problem 14, that's not going to change the absolute value of the determinant.

That would solve the dimensional crisis.

24mrcwi.png

Explanation why the product of b's = det(B)

2hp25ns.png


Consider the kth bracket, any permutation within that bracket is equivalent to a permutation of Q(k).
 

1. Can you explain the concept of determinant in simple terms?

The determinant of a matrix is a value that can be calculated from the elements of the matrix. It is often used to solve systems of linear equations and to find the area or volume of geometric shapes. In simple terms, it can be thought of as a measure of how much the matrix stretches or compresses space.

2. How do you calculate the determinant of a matrix?

There are several methods for calculating the determinant of a matrix, including using cofactor expansion, row reduction, or the Leibniz formula. The specific method used depends on the size and complexity of the matrix.

3. Why is the product of determinants equal to the determinant of the product of two matrices?

This can be explained by the properties of determinants. When two matrices are multiplied, the resulting matrix has elements that are combinations of the elements from the original matrices. The determinant of the resulting matrix is then calculated using these combinations, which results in the product of the determinants of the original matrices.

4. Are there any exceptions to the rule det(AB) = det(A)*det(B)?

Yes, there are some exceptions to this rule. For example, if either matrix A or B is not square, then the product of determinants cannot be calculated. Additionally, if the two matrices do not have the same dimensions, then the product of determinants does not exist.

5. How is the concept of determinant used in real-world applications?

The determinant has various applications in fields such as engineering, physics, and economics. It is used to solve systems of linear equations, find the inverse of a matrix, and calculate the area or volume of geometric shapes. It is also used in computer graphics and image processing to transform and manipulate images.

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