Proving Let A & B be Square Matrices of n x n & AB = 0n

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SUMMARY

In the discussion, it is established that for two non-zero square matrices A and B of size n x n, if their product AB equals the zero matrix 0n, then both determinants det(A) and det(B) must equal zero. The reasoning involves the contradiction arising from assuming det(A) is non-zero, which implies A is invertible, leading to B being the zero matrix, contradicting the assumption. The discussion also highlights the relationship between nullspaces and the dimensions involved, emphasizing that the nullspace of the product AB is maximal, necessitating that both matrices have zero determinants.

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zenn
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Need some help on how to prove this:

Let A and B be two non-zero square matrices of n x n, if AB = 0n, then det(A) and det(B) must both equal to 0.

I know if det(AB) = 0, then det(A) or det(B) must be 0, but here the question states that they must both be 0.

Appreciate any help, thanks.
 
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Suppose that not both are zero, let's take Det(A) to be nonzero. Then A has an inverse. In the equation:

A B = 0,


multiplying both sides by the inverse of A on the left then gives:

B = 0

But B was assumed to be different from the null matrix. So, we arrive at a contradiction. The assumption that Det(A) is nonzero has to be false!
 
Count Iblis used, without saying it explicitely, the fact that a matrix is invertible if and only if its determinant is non-zero.
 
The condition is even stronger - in addition to having zero determinants, the matrices can't have the same column space (or row space) either.
 
when maps compose, the nullspace is at most as big as the sum of the dimensions of the individual nullspaces. you have a composition whose nullspace is maximal, yet neither individual one was. hence both nullspaces are positive dimensional.

i.e. N(A) was not everything, but N(AB) is everything, so the extra dimensions had to come from N(B).

this is the fundamental theorem of linear algebra, the inverse image under A of a finite dimensional space Y, is larger than Y at most by dimN(A).

it is applied here to Y = N(B), since the nullspace of BA is the inverse image of N(B) under A.

i just taught this topic today.
 

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