Compound matrix
Matrix whose entries are all minors of another matrix From Wikipedia, the free encyclopedia
In linear algebra, a branch of mathematics, a (multiplicative) compound matrix is a matrix whose entries are all minors, of a given size, of another matrix.[1][2][3][4] Compound matrices are closely related to exterior algebras,[5] and their computation appears in a wide array of problems, such as in the analysis of nonlinear time-varying dynamical systems and generalizations of positive systems, cooperative systems and contracting systems.[4][6]
Definition
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Perspective
Let A be an m × n matrix with real or complex entries.[a] If I is a subset of size r of {1, ..., m} and J is a subset of size s of {1, ..., n}, then the (I, J )-submatrix of A, written AI, J , is the submatrix formed from A by retaining only those rows indexed by I and those columns indexed by J. If r = s, then det AI, J is the (I, J )-minor of A.
The r th compound matrix of A is a matrix, denoted Cr (A), is defined as follows. If r > min(m, n), then Cr (A) is the unique 0 × 0 matrix. Otherwise, Cr (A) has size . Its rows and columns are indexed by r-element subsets of {1, ..., m} and {1, ..., n}, respectively, in their lexicographic order. The entry corresponding to subsets I and J is the minor det AI, J.
In some applications of compound matrices, the precise ordering of the rows and columns is unimportant. For this reason, some authors do not specify how the rows and columns are to be ordered.[7]
For example, consider the matrix
The rows are indexed by {1, 2, 3} and the columns by {1, 2, 3, 4}. Therefore, the rows of C2 (A) are indexed by the sets
and the columns are indexed by
Using absolute value bars to denote determinants, the second compound matrix is
Properties
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Perspective
Let c be a scalar, A be an m × n matrix, and B be an n × p matrix. For k a positive integer, let Ik denote the k × k identity matrix. The transpose of a matrix M will be written MT, and the conjugate transpose by M*. Then:[8]
- C0 (A) = I1, a 1 × 1 identity matrix.
- C1(A) = A.
- Cr (cA) = crCr (A).
- If rk A = r, then rk Cr (A) = 1.
- If 1 ≤ r ≤ n, then .
- If 1 ≤ r ≤ min(m, n), then Cr (AT) = Cr (A)T.
- If 1 ≤ r ≤ min(m, n), then Cr (A*) = Cr (A)*.
- Cr (AB) = Cr (A) Cr (B), which is closely related to Cauchy–Binet formula.
Assume in addition that A is a square matrix of size n. Then:[9]
- Cn (A) = det A.
- If A has one of the following properties, then so does Cr (A):
- Upper triangular,
- Lower triangular,
- Diagonal,
- Orthogonal,
- Unitary,
- Symmetric,
- Hermitian,
- Skew-symmetric (when r is odd),
- Skew-hermitian (when r is odd),
- Positive definite,
- Positive semi-definite,
- Normal.
- If A is invertible, then so is Cr (A), and Cr (A−1) = Cr (A)−1.
- (Sylvester–Franke theorem) If 1 ≤ r ≤ n, then .[10][11]
Relation to exterior powers
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Perspective
Give Rn the standard coordinate basis e1, ..., en. The r th exterior power of Rn is the vector space
whose basis consists of the formal symbols
where
Suppose that A is an m × n matrix. Then A corresponds to a linear transformation
Taking the r th exterior power of this linear transformation determines a linear transformation
The matrix corresponding to this linear transformation (with respect to the above bases of the exterior powers) is Cr (A). Taking exterior powers is a functor, which means that[12]
This corresponds to the formula Cr (AB) = Cr (A)Cr (B). It is closely related to, and is a strengthening of, the Cauchy–Binet formula.
Relation to adjugate matrices
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Perspective
Let A be an n × n matrix. Recall that its r th higher adjugate matrix adjr (A) is the matrix whose (I, J ) entry is
where, for any set K of integers, σ(K) is the sum of the elements of K. The adjugate of A is its 1st higher adjugate and is denoted adj(A). The generalized Laplace expansion formula implies
If A is invertible, then
A concrete consequence of this is Jacobi's formula for the minors of an inverse matrix:
Adjugates can also be expressed in terms of compounds. Let S denote the sign matrix:
and let J denote the exchange matrix:
Then Jacobi's theorem states that the r th higher adjugate matrix is:[13][14]
It follows immediately from Jacobi's theorem that
Taking adjugates and compounds does not commute. However, compounds of adjugates can be expressed using adjugates of compounds, and vice versa. From the identities
and the Sylvester-Franke theorem, we deduce
The same technique leads to an additional identity,
Compound and adjugate matrices appear when computing determinants of linear combinations of matrices. It is elementary to check that if A and B are n × n matrices then
This has the immediate consequence
Numerical computation
In general, the computation of compound matrices is non-effective due to its high complexity. Nonetheless, there are some efficient algorithms available for real matrices with special structure.[17]
Notes
- The definition, and the purely algebraic part of the theory, of compound matrices requires only that the matrix have entries in a commutative ring. In this case, the matrix corresponds to a homomorphism of finitely generated free modules.
Citations
References
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