order of a square matrix in Vietnamese

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Sentence patterns related to "order of a square matrix"

1. Adjoint of a Matrix Let A = [ a i j ] be a square matrix of order n

2. The cofactor matrix of a square matrix A is the matrix of Cofactors of A

3. Let A be a square matrix of order n, if the rank of matrix A is less than or equal to n-2, then the Adjoint of matrix A results in 0.

4. Adjoint matrix Compute the classical Adjoint (also called adjugate) of a square matrix

5. Adjoint definition, a square matrix obtained from a given square matrix and having the property that its product with the given matrix is equal to the determinant of …

6. Use our online Adjoint matrix calculator to find the adjugate matrix of the square matrix.

7. A matrix with elements that are the Cofactors, term-by-term, of a given square matrix

8. Any square matrix can uniquely be written as sum of a symmetric and a skew-symmetric matrix.

9. The adjugate, classical Adjoint, or adjunct of a square matrix is the transpose of its cofactor matrix

10. A matrix is a rectangular array of numbers written between square brackets.

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11. Example 4: Show that the Adjoint of the Adjoint of A is guaranteed to equal A if A is an invertible 2 by 2 matrix, but not if A is an invertible square matrix of higher order

12. An Antisymmetric matrix, also known as a skew-symmetric or antimetric matrix, is a square matrix that satisfies the identity A=-A^(T) (1) where A^(T) is the matrix transpose

13. The Jacobian matrix is the matrix of all first-order partial derivatives of a vector-valued function.

14. For this to make sense, we need A to be a square matrix.

15. We represent an Alcoved polyhedron by a real square matrix A of order 4 and we compute the exact volume of P: it is a polynomial expression in the aij, homogeneous of

16. In probability theory and statistics, a Covariance matrix (also known as auto-Covariance matrix, dispersion matrix, variance matrix, or variance–Covariance matrix) is a square matrix giving the Covariance between each pair of elements of a given random vector.Any Covariance matrix is symmetric and positive semi-definite and its main diagonal contains variances (i.e., the Covariance of each

17. The Adjoint of a square matrix A = [a ij] n x n is defined as the transpose of the matrix [A ij] n x n, where Aij is the cofactor of the element a ij.Adjoing of the matrix A is denoted by adj A

18. The adjoint M* of a complex matrix M is the transpose of the conjugate of M: M * = M T. A square matrix A is called normal if it commutes with its adjoint: A*A = AA*.

19. Ideally, no two adjacent letters in a row of the matrix are in alphabetical order.

20. Given sets of variates denoted , , , the first-order Covariance matrix is defined by

21. To Absorbing states, Q is the square submatrix giving these probabilities from non- Absorbing to non-Absorbing states, I is an identity matrix, and 0 is a rectangular matrix of zeros

22. The Adjoint of a matrix (also called the adjugate of a matrix) is defined as the transpose of the cofactor matrix of that particular matrix

23. A Covariance matrix is the basis of a correlation matrix.

24. The Adjoint of a scalar multiplication is equal to the product of the scalar raised to n-1 and the Adjoint of the matrix, where n is the order of the matrix

25. A matrix display comprises a matrix of optically addressable pixels (Pij).