Invertible matrix
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 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.
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 a square matrix randomly selected from a continuous uniform distribution on its entries 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 = In. If A has rank m (m ≤ n), then it has a right inverse: an n-by-m matrix B such that AB = Im.
Matrix inversion is the process of finding the matrix B that satisfies the prior equation for a given invertible matrix A.
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 form a group, the general linear group of degree n.
Properties
The invertible matrix theorem
Let A be a square n by n matrix over a field K (for example the field R of real numbers). The following statements are equivalent, that is, for any given matrix they are either all true or all false:
- A is invertible, that is, A has an inverse, is nonsingular, or is nondegenerate.A isrow-equivalentto the n-by-nidentity matrixIn.A iscolumn-equivalentto the n-by-nidentity matrixIn.A has npivot positions.detA ≠ 0. In general, a square matrix over acommutative ringis invertible if and only if itsdeterminantis aunitin that ring.A has full rank; that is,rankA = n.The equationAx = 0has only the trivial solution x = 0.Thekernelof A is trivial, that is, it contains only the null vector as an element, ker(A) = {0}.NullA = {0}.The equation Ax = b has exactly one solution for each b in *Kn*.The columns of A arelinearly independent.The columns of Aspan*Kn*.ColA = *Kn
- to *K
Other properties
Furthermore, the following properties hold for an invertible matrix A:
(A−1)−1 = A;
(kA)−1 = k−1A−1 for nonzero scalar k;
(Ax)+ = x+A−1 where + denotes the Moore–Penrose inverse and x is a vector;
(AT)−1 = (A−1)T;
For any invertible n-by-n matrices A and B, (AB)−1 = B−1A−1. More generally, if A1, ..., Ak are invertible n-by-n matrices, then (A1A2⋅⋅⋅Ak−1Ak)−1 = A−1kA−1k−1⋯A−12A−11;
det A−1 = (det A)−1.
A matrix that is its own inverse, that is, such that A = A−1 and A2 = I, is called an involutory matrix.
In relation to its adjugate
- .
In relation to the identity matrix
It follows from the associativity of matrix multiplication that if
for finite square matrices A and B, then also
Density
Over the field of real numbers, the set of singular n-by-n matrices, considered as a subset of Rn×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.
Examples
Consider the following 2-by-2 matrix:
As an example of a non-invertible, or singular, matrix, consider the matrix
Methods of matrix inversion
Gaussian elimination
Gauss–Jordan 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 upper and lower triangular matrices, which are easier to invert.
Newton's method
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:
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.
Cayley–Hamilton method
Eigendecomposition
If matrix A can be eigendecomposed, and if none of its eigenvalues are zero, then A is invertible and its inverse is given by
![{\displaystyle \left[\Lambda ^{-1}\right]_{ii}={\frac {1}{\lambda _{i}}}.}](https://wikimedia.org/api/rest_v1/media/math/render/svg/839af615243151e8666a32caa26ab67108145c32)
Cholesky decomposition
If matrix A is positive definite, then its inverse can be obtained as
where L is the lower triangular Cholesky decomposition of A, and L* denotes the conjugate transpose of L.
Analytic solution
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:
so that
where |A| is the determinant of A, C is the matrix of cofactors, and CT represents the matrix transpose.
Inversion of 2 × 2 matrices
The cofactor equation listed above yields the following result for 2 × 2 matrices. Inversion of these matrices can be done as follows:[6]
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.
The Cayley–Hamilton method gives
![{\displaystyle \mathbf {A} ^{-1}={\frac {1}{\det \mathbf {A} }}\left[\left(\operatorname {tr} \mathbf {A} \right)\mathbf {I} -\mathbf {A} \right].}](https://wikimedia.org/api/rest_v1/media/math/render/svg/b6a9c4572ae4bc8d969f0b14145493efd4791a32)
Inversion of 3 × 3 matrices
A computationally efficient 3 × 3 matrix inversion is given by
(where the scalar A is not to be confused with the matrix A). 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:
The Cayley–Hamilton decomposition gives
![{\displaystyle \mathbf {A} ^{-1}={\frac {1}{\det(\mathbf {A} )}}\left[{\frac {1}{2}}\left((\operatorname {tr} \mathbf {A} )^{2}-\operatorname {tr} \mathbf {A} ^{2}\right)\mathbf {I} -\mathbf {A} \operatorname {tr} \mathbf {A} +\mathbf {A} ^{2}\right].}](https://wikimedia.org/api/rest_v1/media/math/render/svg/52dbfa16c1fd5676f2b94d19f2ee8658bbd73fb8)
![{\displaystyle \mathbf {A} =\left[\mathbf {x} _{0},\;\mathbf {x} _{1},\;\mathbf {x} _{2}\right]}](https://wikimedia.org/api/rest_v1/media/math/render/svg/a52d5880e38162c990112f9e2401b31f03d75e7d)
Inversion of 4 × 4 matrices
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:
![{\displaystyle \mathbf {A} ^{-1}={\frac {1}{\det(\mathbf {A} )}}\left[{\frac {1}{6}}\left((\operatorname {tr} \mathbf {A} )^{3}-3\operatorname {tr} \mathbf {A} \operatorname {tr} \mathbf {A} ^{2}+2\operatorname {tr} \mathbf {A} ^{3}\right)\mathbf {I} -{\frac {1}{2}}\mathbf {A} \left((\operatorname {tr} \mathbf {A} )^{2}-\operatorname {tr} \mathbf {A} ^{2}\right)+\mathbf {A} ^{2}\operatorname {tr} \mathbf {A} -\mathbf {A} ^{3}\right].}](https://wikimedia.org/api/rest_v1/media/math/render/svg/348c15fc9c4874826e9b8d7e1ff978da5184095b)
Blockwise inversion
Matrices can also be inverted blockwise by using the following analytic inversion formula:
| **(1)** |
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−1B must be nonsingular.[7]) This strategy is particularly advantageous if A is diagonal and D − CA−1B (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), 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−1C are nonsingular,[8] the result is
| **(2)** |
Equating Equations (1) and (2) leads to
| **(3)** |
where Equation (3) is the Woodbury matrix identity, which is equivalent to the binomial inverse theorem.
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.[9] There exist matrix multiplication algorithms with a complexity of O(n2.3727) operations, while the best proven lower bound is Ω(n2 log n).[10]
By Neumann series
If a matrix A has the property that
then A is nonsingular and its inverse may be expressed by a Neumann series:[11]
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
then A is nonsingular and its inverse is
If it is also the case that A − X has rank 1 then this simplifies to
P-adic approximation
Derivative of the matrix inverse
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
More generally, if
then,
Therefore,
Generalized inverse
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.
Applications
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.
Regression/least squares
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.[14]
Matrix inverses in real-time simulations
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 inverses in MIMO wireless communication
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 a NxM 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.
See also
Binomial inverse theorem
LU decomposition
Matrix decomposition
Matrix square root
Pseudoinverse
Singular value decomposition
Woodbury matrix identity