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In linear algebra, an n-by-n (square) matrix

A

is called invertible, non-singular, or regular if there exists an n-by-n matrix

B

such that

$AB = BA = I_n$

where

I_n

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^

. It follows from the theory of matrices that if

$AB = I$

for square matrices

A

and

B

, then also

$BA = I .$

A square matrix that is not invertible is called singular or degenerate. 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 ring.

As a rule of thumb, almost all square matrices are invertible. Over the field of real numbers, this can be made precise as follows: the set of singular n-by-n matrices, considered as a subset of

R^

, is a null set, i.e., has Lebesgue measure zero. Intuitively, this means that if you pick a random square matrix over the reals, the probability that it will be singular is zero. This is true because singular matrices can be thought of as the roots of the polynomial function given by the determinant. In practice however, one may encounter non-invertible matrices. And in numerical calculations, matrices which are invertible, but close to a non-invertible matrix, can still be problematic; such matrices are said to be ill conditioned.

Matrix inversion is the process of finding the matrix

B

that satisfies the prior equation for a given invertible matrix

A

    Invertible matrix
        Properties of invertible matrices
            Proof for matrix product rule
            Gaussian elimination
            Analytic solution
                Inversion of 2 x 2 matrices
                Inversion of 3 x 3 matrices
            Blockwise inversion
        The derivative of the matrix inverse
        The Moore-Penrose pseudoinverse
        See also

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Properties of invertible matrices

Let

A

be a square n by n matrix over a field

K

(for example the field

R

of real numbers). Then the following statements are equivalent:

A

is invertible.

A

is row-equivalent to the n-by-n identity matrix

I_n

A

has n pivot positions.

det

A

≠ 0.

rank

A

= n.

The equation

Ax = 0

has only the trivial solution

x = 0

(i.e. Null

A = 0

The equation

Ax = b

has exactly one solution for each

b

K^n

The columns of

A

are linearly independent.

The columns of

A

span

K^n

(i.e. Col

A = K^n

The columns of

A

form a basis of

K^n

The linear transformation mapping

x

Ax

is a bijection from

K^n

K^n

There is an n by n matrix

B

such that

AB = I_n

The transpose

A^T

is an invertible matrix.

The matrix times its transpose,

A^T 	imes A

is an invertible matrix.

The number 0 is not an eigenvalue of

A

In general, a square matrix over a commutative ring is invertible if and only if its determinant is a unit in that ring.

The inverse of an invertible matrix

A

is itself invertible, with

$left(A^$

ight)^ = A .
The inverse of an invertible matrix

A

multiplied by a scalar

k

yields the product of the inverse of both the matrix and the scalar

$left(kA$

ight)^ = k^A^.
The product of two invertible matrices

A

and

B

of the same size is again invertible, with the inverse given by

$left(AB$

ight)^ = B^A^
(note that the order of the factors is reversed.) As a consequence, the set of invertible n-by-n matrices forms a group, known as the general linear group Gl(n).

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Proof for matrix product rule

If

A_1

A_2

, ...,

A_n

are nonsingular square matrices over a field, then

$(A_1A_2cdots A_n)^ = A_n^A_^cdots A_1^$

It becomes evident why this is the case if one attempts to find an inverse for the product of the

A_i

s from first principles, that is, that we wish to determine

B

such that

$(A_1A_2cdots A_n)B=I$

where

B

is the inverse matrix of the product. To remove

A_1

from the product, we can then write

$A_1^(A_1A_2cdots A_n)B=A_1^I$

which would reduce the equation to

$(A_2A_3cdots A_n)B=A_1^I$

Likewise, then, from

$A_2^(A_2A_3cdots A_n)B=A_2^A_1^I$

which simplifies to

$(A_3A_4cdots A_n)B=A_2^A_1^I$

If one repeat the process up to

A_n

, the equation becomes

$B=A_n^A_^cdots A_2^A_1^I$

$B=A_n^A_^cdots A_2^A_1^$

but

B

is the inverse matrix, i.e

B = (A_1A_2cdots A_n)^

so the property is established.

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Gaussian elimination
Gaussian elimination is an algorithm that can be used to determine whether a given matrix is invertible and to find the inverse. An alternative is the LU decomposition which generates an upper and a lower triangular matrices which are easier to invert. For special purposes, it may be convenient to invert matrices by treating mn-by-mn matrices as m-by-m matrices of n-by-n matrices, and applying one or another formula recursively (other sized matrices can be padded out with dummy rows and columns). For other purposes, a variant of Newton's method may be convenient (particularly when dealing with families of related matrices, so inverses of earlier matrices can be used to seed generating inverses of later matrices).

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Analytic solution
Writing another special matrix of cofactors, known as an adjugate matrix, can also be an efficient way to calculate the inverse of small matrices (since this method is essentially recursive, it becomes inefficient for large matrices). To determine the inverse, we calculate a matrix of cofactors:

$A^=left(C_$

ight)^= egin C_ & C_ & cdots & C_ \ C_ & ddots & & C_ \ vdots & & ddots & vdots \ C_ & cdots & cdots & C_ \ end

where |A| is the determinant of A, C_ij is the matrix cofactor, and A^T represents the matrix transpose.

In most practical applications, it is not necessary to invert a matrix to solve a system of linear equations, however it is necessary that the matrix involved is invertible.

Decomposition techniques like LU decomposition, are much faster than inversion, and various fast algorithms for special classes of linear systems have also been developed.

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Inversion of 2 x 2 matrices
The cofactor equation listed above yields the following result for 2 x 2 matrices. Inversion of these matrices can be done easily as follows:

$A^ = egin$

a & b \ c & d \ end^ = rac egin d & -b \ -c & a \ end

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Inversion of 3 x 3 matrices
The cofactor equation listed above yields the following result for 3 x 3 matrices. Inversion of these matrices can be done quite easily as follows:

$A^ = egin$

a & b & c\ d & e & f \ g & h & i \ end^ = rac egin ei - fh & ch - bi & bf - ce \ fg - di & ai - cg & cd - af \ dh - eg & bg - ah & ae - bd end

$|A| = a(ei-fh) - b(di-fg) + c(dh-eg)$

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Blockwise inversion
Matrices can also be inverted blockwise by using the following analytic inversion formula:

$egin A & B \ C & D end^ = egin A^+A^B(D-CA^B)^CA^ & -A^B(D-CA^B)^ \ -(D-CA^B)^CA^ & (D-CA^B)^ end$

where A, B, C and D are matrix sub-blocks of arbitrary size. This strategy is particularly advantageous if A is diagonal and

(D-CA^B)

(the Schur complement of

A

) is a small matrix, since they are the only matrices requiring inversion.

This technique was invented by Volker Strassen, who also invented the Strassen algorithm for fast(er) matrix multiplication.

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The derivative of the matrix inverse

Suppose that the matrix A depends on a parameter t. Then the derivative of the inverse of A with respect to t is given by

$rac = - A^ rac A^.$

This formula can be found by differentiating the identity

$A^A = I$ .

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The Moore-Penrose pseudoinverse

Some of the properties of inverse matrices are shared by (Moore-Penrose) pseudoinverses, which can be defined for any m-by-n matrix.

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