# Invertible matrix

In linear algebra, an *n*-by-*n* square matrix **A** is called **invertible** (also **nonsingular** or **nondegenerate**), if there exists an *n*-by-*n* square matrix **B** such that

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 (multiplicative) * inverse* of

**A**, denoted by

**A**

^{−1}.

^{[1]}

**Matrix inversion**is the process of finding the matrix

**B**that satisfies the prior equation for a given invertible matrix

**A**.

A square matrix that is *not* invertible is called **singular** or **degenerate**. A square matrix is singular if and only if its determinant is zero.^{[2]} Singular matrices are rare in the sense that if a square matrix's entries are randomly selected from any finite region on the number line or complex plane, the probability that the matrix is singular is 0, that is, it will "almost never" be singular. 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* (*n* ≤ *m*), then **A** has a left inverse, an *n*-by-*m* matrix **B** such that **BA** = **I**_{n}. If **A** has rank *m* (*m* ≤ *n*), then it has a right inverse, an n-by-m matrix **B** such that **AB** = **I**_{m}.

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. However, in the case of the ring being commutative, the condition for a square matrix to be invertible is that its determinant is invertible in the ring, which in general is a stricter requirement than being nonzero. For a noncommutative ring, the usual determinant is not defined. The conditions for existence of left-inverse or right-inverse are more complicated, since a notion of rank does not exist over rings.

The set of *n* × *n* invertible matrices together with the operation of matrix multiplication (and entries from ring *R*) form a group, the general linear group of degree *n*, denoted GL_{n}(*R*).

Let **A** be a square *n* by *n* matrix over a field *K* (e.g., the field **R** of real numbers). The following statements are equivalent (i.e., they are either all true or all false for any given matrix):^{[3]}

Furthermore, the following properties hold for an invertible matrix **A**:

A matrix that is its own inverse (i.e., a matrix **A** such that **A** = **A**^{−1} and **A**^{2} = **I**), is called an involutory matrix.

Over the field of real numbers, the set of singular *n*-by-*n* matrices, considered as a subset of **R**^{n×n}, is a null set, that is, has Lebesgue measure zero. This is true because singular matrices are the roots of the determinant function. This is a continuous function because it is a polynomial in the entries of the matrix. Thus in the language of measure theory, almost all *n*-by-*n* matrices are invertible.

Furthermore, the *n*-by-*n* invertible matrices are a dense open set in the topological space of all *n*-by-*n* matrices. Equivalently, the set of singular matrices is closed and nowhere dense in the space of *n*-by-*n* matrices.

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.

We can easily see the rank of this 2*2 matrix is one, which is n-1≠n, so it is a non-invertible matrix.

As an example of a non-invertible, or singular, matrix, consider the matrix

Gaussian elimination is a useful and easy way to compute the inverse of a matrix. To compute a matrix inverse using this method, an augmented matrix is first created with the left side being the matrix to invert and the right side being the identity matrix. Then, Gaussian elimination is used to convert the left side into the identity matrix, which causes the right side to become the inverse of the input matrix.

A generalization of Newton's method as used for a multiplicative inverse algorithm may be convenient, if it is convenient to find a suitable starting seed:

Victor Pan and John Reif have done work that includes ways of generating a starting seed.^{[5]}^{[6]} Byte magazine summarised one of their approaches.^{[7]}

Newton's method is particularly useful when dealing with families of related matrices that behave enough like the sequence manufactured for the homotopy above: sometimes a good starting point for refining an approximation for the new inverse can be the already obtained inverse of a previous matrix that nearly matches the current matrix, for example, the pair of sequences of inverse matrices used in obtaining matrix square roots by Denman–Beavers iteration; this may need more than one pass of the iteration at each new matrix, if they are not close enough together for just one to be enough. Newton's method is also useful for "touch up" corrections to the Gauss–Jordan algorithm which has been contaminated by small errors due to imperfect computer arithmetic.

If matrix **A** can be eigendecomposed, and if none of its eigenvalues are zero, then **A** is invertible and its inverse is given by

where **L** is the lower triangular Cholesky decomposition of **A**, and **L*** denotes the conjugate transpose of **L**.

Writing the transpose of the matrix of cofactors, known as an adjugate matrix, can also be an efficient way to calculate the inverse of *small* matrices, but this recursive method is inefficient for large matrices. To determine the inverse, we calculate a matrix of cofactors:

where |**A**| is the determinant of **A**, **C** is the matrix of cofactors, and **C**^{T} represents the matrix transpose.

The *cofactor equation* listed above yields the following result for 2 × 2 matrices. Inversion of these matrices can be done as follows:^{[9]}

This is possible because 1/(*ad* − *bc*) is the reciprocal of the determinant of the matrix in question, and the same strategy could be used for other matrix sizes.

If the determinant is non-zero, the matrix is invertible, with the elements of the intermediary matrix on the right side above given by

The determinant of **A** can be computed by applying the rule of Sarrus as follows:

With increasing dimension, expressions for the inverse of **A** get complicated. For *n* = 4, the Cayley–Hamilton method leads to an expression that is still tractable:

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

where **A**, **B**, **C** and **D** are matrix sub-blocks of arbitrary size. (**A** must be square, so that it can be inverted. Furthermore, **A** and **D** − **CA**^{−1}**B** must be nonsingular.^{[10]}) This strategy is particularly advantageous if **A** is diagonal and **D** − **CA**^{−1}**B** (the Schur complement of **A**) is a small matrix, since they are the only matrices requiring inversion.

This technique was reinvented several times and is due to Hans Boltz (1923),^{[citation needed]} who used it for the inversion of geodetic matrices, and Tadeusz Banachiewicz (1937), who generalized it and proved its correctness.

The nullity theorem says that the nullity of **A** equals the nullity of the sub-block in the lower right of the inverse matrix, and that the nullity of **B** equals the nullity of the sub-block in the upper right of the inverse matrix.

The inversion procedure that led to Equation (**1**) performed matrix block operations that operated on **C** and **D** first. Instead, if **A** and **B** are operated on first, and provided **D** and **A** − **BD**^{−1}**C** are nonsingular,^{[11]} the result is

where Equation (**3**) is the Woodbury matrix identity, which is equivalent to the binomial inverse theorem.

If **A** and **D** are both invertible, then the above two block matrix inverses can be combined to provide the simple factorization

By the Weinstein–Aronszajn identity, one of the two matrices in the block-diagonal matrix is invertible exactly when the other is.

Since a blockwise inversion of an `n` × `n` matrix requires inversion of two half-sized matrices and 6 multiplications between two half-sized matrices, it can be shown that a divide and conquer algorithm that uses blockwise inversion to invert a matrix runs with the same time complexity as the matrix multiplication algorithm that is used internally.^{[12]} Research into matrix multiplication complexity shows that there exist matrix multiplication algorithms with a complexity of *O*(*n*^{2.3727}) operations, while the best proven lower bound is Ω(`n`^{2} log `n`).^{[13]}

then **A** is nonsingular and its inverse may be expressed by a Neumann series:^{[14]}

Truncating the sum results in an "approximate" inverse which may be useful as a preconditioner. Note that a truncated series can be accelerated exponentially by noting that the Neumann series is a geometric sum. As such, it satisfies

More generally, if **A** is "near" the invertible matrix **X** in the sense that

Suppose that the invertible matrix **A** depends on a parameter *t*. Then the derivative of the inverse of **A** with respect to *t* is given by^{[17]}

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

For most practical applications, it is *not* necessary to invert a matrix to solve a system of linear equations; however, for a unique solution, it *is* necessary that the matrix involved be 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.

Although an explicit inverse is not necessary to estimate the vector of unknowns, it is the easiest way to estimate their accuracy, found in the diagonal of a matrix inverse (the posterior covariance matrix of the vector of unknowns). However, faster algorithms to compute only the diagonal entries of a matrix inverse are known in many cases.^{[18]}

Matrix inversion plays a significant role in computer graphics, particularly in 3D graphics rendering and 3D simulations. Examples include screen-to-world ray casting, world-to-subspace-to-world object transformations, and physical simulations.

Matrix inversion also plays a significant role in the MIMO (Multiple-Input, Multiple-Output) technology in wireless communications. The MIMO system consists of *N* transmit and *M* receive antennas. Unique signals, occupying the same frequency band, are sent via *N* transmit antennas and are received via *M* receive antennas. The signal arriving at each receive antenna will be a linear combination of the *N* transmitted signals forming an *N* × *M* transmission matrix **H**. It is crucial for the matrix **H** to be invertible for the receiver to be able to figure out the transmitted information.