Determinant and Matrix Inverse

Invertible matrix
From Wikipedia, the free encyclopedia
In linear algebra an n-by-n (square) matrix A is called invertible (some authors use nonsingular or
nondegenerate) if there exists an n-by-n matrix B such that
where In denotes the n-by-n identity matrix and the multiplication used is ordinary matrix multiplication. If this is
the case, then the matrix B is uniquely determined by A and is called the inverse of A, denoted by A?1. It follows
from the theory of matrices that if
for finite square matrices A and B, then also
[1]
Non-square matrices (m-by-n matrices for which m ? n) do not have an inverse. However, in some cases such a
matrix may have a left inverse or right inverse. If A is m-by-n and the rank of A is equal to n, then A has a left
inverse: an n-by-m matrix B such that BA = I. If A has rank m, then it has a right inverse: an n-by-m matrix B such
that AB = I.
A square matrix that is not invertible is called singular or degenerate. A square matrix is singular if and only if its
determinant is 0. Singular matrices are rare in the sense that if you pick a random square matrix over a continuous
uniform distribution on its entries, it will almost surely not be singular.
While the most common case is that of matrices over the real or complex numbers, all these definitions can be
given for matrices over any commutative ring. However, in this case the condition for a square matrix to be
invertible is that its determinant is invertible in the ring, which in general is a much stricter requirement than being
nonzero. The conditions for existence of left-inverse resp. right-inverse are more complicated since a notion of rank
does not exist over rings.
Matrix inversion is the process of finding the matrix B that satisfies the prior equation for a given invertible matrix
A.