Analyzing the Setup
We are given two 3×3 non-zero real matrices, A and B, such that their product AB is the zero matrix O.
At first glance, one might assume that at least one of the matrices must be the zero matrix. However, in the elegant and counter-intuitive world of linear algebra, this is not necessarily true.
The Determinant Bridge
To understand the nature of these matrices, we utilize the property of determinants. We start with the given equation:
AB=O
Taking the determinant of both sides, we have
∣AB∣=∣O∣. Since the determinant of a zero matrix is
0, we obtain:
∣AB∣=0
Invoking the multiplicative property of determinants,
∣AB∣=∣A∣∣B∣, we arrive at the crucial realization:
∣A∣∣B∣=0
This equation implies that at least one of the determinants must be zero.
The Proof by Contradiction
Suppose, for the sake of argument, that $|A|
eq 0$. If this were true, matrix A would be non-singular and possess an inverse, A−1.
We test this assumption by pre-multiplying the original equation
AB=O by
A−1:
A−1(AB)=A−1O
Using the associative property of matrix multiplication, we get
(A−1A)B=O. Since
A−1A=I (the identity matrix), this simplifies to:
IB=O⇒B=O
However, the problem explicitly states that B is a non-zero matrix. This creates a contradiction, forcing us to conclude that ∣A∣=0. Thus, matrix A must be a singular matrix.
The Geometric Reality of AX=0
With ∣A∣=0 established, we examine the homogeneous system of linear equations AX=0.
In linear systems, the determinant acts as a gatekeeper. When ∣A∣=0, the transformation A collapses space, projecting 3D space onto a lower-dimensional subspace such as a plane or a line.
Because the transformation is not injective, there exists a non-zero null space (or kernel). Any vector X in this null space satisfies AX=0. Since the null space contains infinitely many vectors, the system AX=0 must have infinitely many solutions.
The Final Elegance
We have proven that ∣A∣=0, which directly implies that the system AX=0 has infinitely many solutions.
As a further observation, consider the adjoint matrix,
adj(A). We know the identity:
∣adj(A)∣=∣A∣n−1
For a
3×3 matrix, this becomes:
∣adj(A)∣=∣A∣3−1=∣A∣2
Since ∣A∣=0, the determinant of the adjoint is also zero, meaning adj(A) is not invertible. The beauty of this problem lies in how it forces us to abandon the simple rules of scalar arithmetic and embrace the structural properties of linear transformations.