Cofactor (linear algebra)
Cofactor (linear algebra)
In linear algebra, a minor of a matrix A is the determinant of some smaller square matrix, cut down from A by removing one or more of its rows and columns. Minors obtained by removing just one row and one column from square matrices (first minors) are required for calculating matrix cofactors, which in turn are useful for computing both the determinant and inverse of square matrices.
Definition and illustration
First minors
To illustrate these definitions, consider the following 3 by 3 matrix,
To compute the minor M2,3 and the cofactor C2,3, we find the determinant of the above matrix with row 2 and column 3 removed.
So the cofactor of the (2,3) entry is
General definition
![[A]_{{I,J}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/df99a3764e33728fd227e83a2b6d0b5c49503276)
Complement
The complement, Bijk...,pqr..., of a minor, Mijk...,pqr..., of a square matrix, A, is formed by the determinant of the matrix A from which all the rows (ijk...) and columns (pqr...) associated with Mijk...,pqr... have been removed. The complement of the first minor of an element aij is merely that element.[5]
Applications of minors and cofactors
Cofactor expansion of the determinant
The cofactor expansion along the ith row gives:
Inverse of a matrix
One can write down the inverse of an invertible matrix by computing its cofactors by using Cramer's rule, as follows. The matrix formed by all of the cofactors of a square matrix A is called the cofactor matrix (also called the matrix of cofactors or comatrix):
Then the inverse of A is the transpose of the cofactor matrix times the reciprocal of the determinant of A:
The transpose of the cofactor matrix is called the adjugate matrix (also called the classical adjoint) of A.
- ,
![[{\mathbf A}^{{-1}}]_{{I,J}}=\pm {\frac {[{\mathbf A}]_{{J',I'}}}{\det {\mathbf A}}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/edcdc4d79cc2c335d21f52ac7b376d421d29959c)
![[{\mathbf A}]_{{I,J}}](https://wikimedia.org/api/rest_v1/media/math/render/svg/79116b5e84bb99178ebe45f84a4dccb668e94067)
![[{\mathbf A}]_{{I,J}}=\det \left((A_{{i_{p},j_{q}}})_{{p,q=1,\ldots ,k}}\right)](https://wikimedia.org/api/rest_v1/media/math/render/svg/1fb2916a417ec4dfca87386149f869070b1180e2)
![[{\mathbf A}^{{-1}}]_{{I,J}}(e_{1}\wedge \ldots \wedge e_{n})=\pm ({\mathbf A}^{{-1}}e_{{j_{1}}})\wedge \ldots \wedge ({\mathbf A}^{{-1}}e_{{j_{k}}})\wedge e_{{i'_{1}}}\wedge \ldots \wedge e_{{i'_{{n-k}}}},](https://wikimedia.org/api/rest_v1/media/math/render/svg/4a27a8ed0953c2c8a353be434f822afb04a16bf9)
![[{\mathbf A}^{{-1}}]_{{I,J}}\det {\mathbf A}(e_{1}\wedge \ldots \wedge e_{n})=\pm (e_{{j_{1}}})\wedge \ldots \wedge (e_{{j_{k}}})\wedge ({\mathbf A}e_{{i'_{1}}})\wedge \ldots \wedge ({\mathbf A}e_{{i'_{{n-k}}}})=\pm [{\mathbf A}]_{{J',I'}}(e_{1}\wedge \ldots \wedge e_{n}).](https://wikimedia.org/api/rest_v1/media/math/render/svg/1cd3a8d5d9b43ae8695a514dc5e59af9a0d2622b)
Other applications
Given an m × n matrix with real entries (or entries from any other field) and rank r, then there exists at least one non-zero r × r minor, while all larger minors are zero.
We will use the following notation for minors: if A is an m × n matrix, I is a subset of {1,...,m} with k elements and J is a subset of {1,...,n} with k elements, then we write [A]I,J for the k × k minor of A that corresponds to the rows with index in I and the columns with index in J.
If I = J, then [A]I,J is called a principal minor.
If the matrix that corresponds to a principal minor is a quadratic upper-left part of the larger matrix (i.e., it consists of matrix elements in rows and columns from 1 to k), then the principal minor is called a leading principal minor (of order k) or corner (principal) minor (of order k).[3] For an n × n square matrix, there are n leading principal minors.
A basic minor of a matrix is the determinant of a square submatrix that is of maximal size with nonzero determinant.[3]
For Hermitian matrices, the leading principal minors can be used to test for positive definiteness and the principal minors can be used to test for positive semidefiniteness. See Sylvester's criterion for more details.
Both the formula for ordinary matrix multiplication and the Cauchy–Binet formula for the determinant of the product of two matrices are special cases of the following general statement about the minors of a product of two matrices. Suppose that A is an m × n matrix, B is an n × p matrix, I is a subset of {1,...,m} with k elements and J is a subset of {1,...,p} with k elements. Then
![[\mathbf{AB}]_{I,J} = \sum_{K} [\mathbf{A}]_{I,K} [\mathbf{B}]_{K,J}\,](https://wikimedia.org/api/rest_v1/media/math/render/svg/9b5b02d20f091af80b1b0e1c27b4e1758a620ddc)
where the sum extends over all subsets K of {1,...,n} with k elements. This formula is a straightforward extension of the Cauchy–Binet formula.
Multilinear algebra approach
A more systematic, algebraic treatment of the minor concept is given in multilinear algebra, using the wedge product: the k-minors of a matrix are the entries in the kth exterior power map.
If the columns of a matrix are wedged together k at a time, the k × k minors appear as the components of the resulting k-vectors. For example, the 2 × 2 minors of the matrix
are −13 (from the first two rows), −7 (from the first and last row), and 5 (from the last two rows). Now consider the wedge product
where the two expressions correspond to the two columns of our matrix. Using the properties of the wedge product, namely that it is bilinear and
and
we can simplify this expression to
where the coefficients agree with the minors computed earlier.
A remark about different notation
In some books instead of cofactor the term adjunct is used.[7] Moreover, it is denoted as Aij and defined in the same way as cofactor:
Using this notation the inverse matrix is written this way:
Keep in mind that adjunct is not adjugate or adjoint. In modern terminology, the "adjoint" of a matrix most often refers to the corresponding adjoint operator.
See also
Submatrix