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Copositive matrix
Matrix in linear algebra From Wikipedia, the free encyclopedia
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In mathematics, specifically linear algebra, a real symmetric matrix A is copositive if
for every nonnegative vector (where the inequalities should be understood coordinate-wise). Some authors do not require A to be symmetric.[1] The collection of all copositive matrices is a proper cone;[2] it includes as a subset the collection of real positive-definite matrices.
Copositive matrices find applications in economics, operations research, and statistics.
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Examples
- Every real positive-semidefinite matrix is copositive by definition.
- Every symmetric nonnegative matrix is copositive. This includes the zero matrix.
- The exchange matrix is copositive but not positive-semidefinite.
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Properties
Summarize
Perspective
It is easy to see that the sum of two copositive matrices is a copositive matrix. More generally, any conical combination of copositive matrices is copositive.
Let A be a copositive matrix. Then we have that
- every principal submatrix of A is copositive as well. In particular, the entries on the main diagonal must be nonnegative.
- the spectral radius ρ(A) is an eigenvalue of A.[3]
Every copositive matrix of order less than 5 can be expressed as the sum of a positive semidefinite matrix and a nonnegative matrix.[4] A counterexample for order 5 is given by a copositive matrix known as Horn-matrix:[5]
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Characterization
The class of copositive matrices can be characterized using principal submatrices. One such characterization is due to Wilfred Kaplan:[6]
- A real symmetric matrix A is copositive if and only if every principal submatrix B of A has no eigenvector v > 0 with associated eigenvalue λ < 0.
Several other characterizations are presented in a survey by Ikramov,[3] including:
- Assume that all the off-diagonal entries of a real symmetric matrix A are nonpositive. Then A is copositive if and only if it is positive semidefinite.
The problem of deciding whether a matrix is copositive is co-NP-complete.[7]
References
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