Examples of using Square matrix in English and their translations into Vietnamese
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A square matrix of order 4.
The entries a i i{\displaystyle a_{ii}}form the main diagonal of a square matrix.
Where Ak is a square matrix for all k= 1,…, n.
A square matrix is called lower triangular if all the entries above the main diagonal are zero.
An n-by-n matrix is known as a square matrix of order n{\displaystyle n}.
Similarly, a square matrix is called upper triangular if all the entries below the main diagonal are zero.
In the mathematical discipline of linear algebra,a triangular matrix is a special kind of square matrix.
Any real square matrix A may be decomposed as.
Right and it turns out only square matrices have inverses, so A is a square matrix, is m by m, on inverse this equation over here.
Therefore, for a square matrix A(of the correct size) we have det(Lij(m)A)= det(A).
If a definition that the inverse of a matrix is,I had this caveat first it must always be a square matrix, it had this caveat, that if.
The nullity of a square matrix was defined by Sylvester in 1884.
The possibility to reinterpret the entries of a matrix as elements of a larger field(for example, to view a real matrix as a complex matrix whose entries happen to be all real)then allows considering each square matrix to possess a full set of eigenvalues.
Therefore for a square matrix A(of the correct size), we have det(Di(m)A)= m det(A).
This is similar toformulating a dynamical process that gradually"perturbs" a given square matrix, and that is guaranteed to result after a finite time in its rational canonical form.
Any square matrix can trivially be considered a block diagonal matrix with only one block.
It follows that for any square matrix A(of the correct size), we have det(TijA)=- det(A).
If instead A is a complex square matrix, then there is a decomposition A= QR where Q is a unitary matrix(so Q∗ Q= Q Q∗= I{\displaystyle Q^{*} Q= QQ^{*}= I}).
In matrix algebra, a diagonal of a square matrix is a set of entries extending from one corner to the farthest corner.
Definition Given a square matrix of size$M$ and each element is$\{0, 1,\cdots, M-1\}$, we construct a matrix such that for the element$i$ in the set$\{0, 1,\cdots, M-1\}$ only appears exactly 1 time on every row and column.
He postulated the Cayley- Hamilton theorem- that every square matrix is a root of its own characteristic polynomial, and verified it for matrices of order 2 and 3.
For example, if R{\displaystyle R} is a square matrix representing a rotation(rotation matrix) and v{\displaystyle v} is a column vector describing the position of a point in space, the product R v{\displaystyle Rv} yields another column vector describing the position of that point after that rotation.
M by M, this is also called a square matrix and it's called square because the number of rows is equal to the number of columns.
In mathematics, a square matrix is a matrix with the same number of rows and columns.
A block tridiagonal matrix is another special block matrix, which is just likethe block diagonal matrix a square matrix, having square matrices(blocks) in the lower diagonal, main diagonal and upper diagonal, with all other blocks being zero matrices. It is essentially a tridiagonal matrix but has submatrices in places of scalars.
Any two square matrices of the same order can be added and multiplied.
Square matrices are often used to represent simple linear transformations, such as shearing or rotation.
Together these facts mean that the upper triangularmatrices form a subalgebra of the associative algebra of square matrices for a given size.
It is oftenreferred to as a Borel subalgebra of the Lie algebra of all square matrices.
Additionally, this also shows that the upper triangular matrices can beviewed as a Lie subalgebra of the Lie algebra of square matrices of a fixed size, where the Lie bracket[a, b] given by the commutator ab- ba. The Lie algebra of all upper triangular matrices is a solvable Lie algebra.