Well, let me tell you about my experience with matrices and their invertibility. When I first learned about matrices, I was fascinated by how they could represent linear transformations. It was like a whole new world opened up to me.

One of the key concepts I learned early on was the nullity of a matrix. The nullity represents the dimension of the null space of a matrix, which is the set of all vectors that are mapped to the zero vector by the matrix. In other words, it’s the set of solutions to the equation Ax = 0, where A is the matrix and x is a vector.

Now, let’s think about what it means for a matrix to be invertible. An invertible matrix is one that has an inverse, which undoes the effects of the original matrix. For example, if we have a matrix A and its inverse A^-1, then A * A^-1 = I, where I is the identity matrix.

So, if a matrix has nullity 0, it means that there is only one vector that is sent to the zero vector by the matrix. In other words, the only solution to the equation Ax = 0 is the zero vector itself. This is an important property because it means that the matrix doesn’t “lose” any information when it transforms vectors.

In fact, it turns out that a matrix is invertible if and only if its nullity is 0. This makes sense intuitively because if a matrix has nullity above 0, it means that there are multiple vectors that are mapped to the zero vector. And if we were to try to find an inverse for such a matrix, we would have to “reverse” the effects of these multiple vectors, which is not possible.

I remember working through some examples to solidify my understanding of this concept. For instance, I considered a 2×2 matrix with nullity 0. I found that if the matrix had a non-zero nullity, it meant that there were infinitely many solutions to the equation Ax = 0, and hence it was not invertible.

On the other hand, when the nullity was 0, I could find a unique solution to the equation Ax = 0, which was the zero vector. This meant that the matrix didn’t “lose” any information and could be reversed by its inverse. It was like solving a puzzle where all the pieces fit perfectly together.

So, to sum it up, a matrix with nullity 0 is indeed invertible. This is because it doesn’t “lose” any information when it transforms vectors, and therefore, its effects can be reversed by finding its inverse. The concept of nullity and invertibility opened my eyes to the beauty and intricacies of linear transformations, and I continue to explore and learn more about them to this day.