You don't want to use the product of eigenvalues to determine if a matrix is singular. This is equivalent to computing the determinant, another terribly bad way to test for singularity.
Instead, learn to use tools like rank or cond to make that determination.
T = [5 -2 -2 0; -51 30 -26 39; -14 -10 6 -10; 34 -31 25 -48];
rank(T)
T is a 4x4 matrix. It has rank 4, so it is technically invertible. How close it is?
cond(T)
In fact, T is quite well conditioned. Singular matrices will have condition numbers on the order of 1e16 or larger.
In context of your actual questiion, what did you do wrong?
[V,D] = eig(T);
you used -8.2242, which you APPARENTLY think is one of the eigenvalues. But is it?
NO. That is approximately an eigenvalue.
format long g
D(2,2)
In fact, it was not an eigenvalue. The value you used was incorrect. Close. But using 5 significant digit approximations to things is a bad idea, something you need to relearn as you learn mathematics.
k = V(:,2);
lambda = D(2,2);
T*k - lambda*k
As you can see, here the difference is on the order of floating point trash, so effectively zero.
