Adjoint matrix

Trace of a matrix

A matrix

Hermitian matrix (conjugate transpose)

Transpose matrix

Conjugate matrix

Inverse matrix

Identity matrix

Consider a matrix, , with complex elements:

Note that each element has the form . The rows are numbered from 1 to M, and the columns are numbered from 1 to N.

The adjoint of a square matrix is the transpose of the matrix of cofactors.

A block-diagonal matrix has square “blocks” of generally non-zero elements situated along its main diagonal with the diagonal of each block coinciding with the diagonal of the matrix. Elsewhere there are zeroes. Each block must be a square matrix. An example of a block-diagonal matrix is

A block matrix is any matrix whose elements have been subdivided into rectangular regions or “blocks.”

The cofactor of an element of a matrix, say, , is found in the following manner. First, delete the mth row and nth column of the original square matrix; that is, rewrite the matrix as an M-1 by M-1 matrix omitting the mth row and nth column. Next, find the determinant of the resulting matrix. The cofactor of element is commonly denoted as .

The matrix of cofactors is found by replacing every element, , of a matrix by its cofactor.

All elements of a conjugate matrix are the complex conjugate of the original matrix. The conjugate matrix is commonly denoted by a superscript “*” (asterisk).

See *Rank of a Matrix*.

A congruent matrix is a matrix, , that has the property that a non-singular matrix, , can be found such that and is diagonal and that the number of positive elements in is invariant of .

See Hermitian Matrix.

A dense matrix has few or no elements equal to 0. Compare to Sparse Matrix.

The diagonal (also called the main diagonal) includes all of the elements of a square matrix for which it is true that .

A diagonal matrix is a square matrix that has non-zero elements only when .

The Hermitian matrix is the conjugate transpose of the original matrix. The Hermitian is commonly denoted with a superscript “H”. Note that the diagonal elements of a Hermitian matrix must be real.

The identity matrix is a square (diagonal) matrix with all diagonal elements equal to 1 and all non-diagonal elements equal to 0. It is also called the unity matrix. The identity matrix is commonly denoted by .

The inverse matrix is the matrix that, when multiplied by the original matrix, yields the identity matrix. The inverse is commonly denoted by a superscript “-1”. That is, the inverse matrix has the property that
. Note that a matrix must be square to have an inverse (but see also *Generalized Inverse Matrix*).

See *Triangular Matrix*.

A non-singular matrix has a rank that is equal to the maximum order of the matrix.

A normal matrix has the property that .

The order of the matrix is the number of rows or columns of a matrix. For example, if a matrix has 5 rows and 3 columns, the order is denoted (read “5 by 3”); and the maximum order is 5.

An orthogonal matrix has the property that . An orthogonal matrix is always invertible with .

The rank of a matrix is the maximum number of linearly independent rows (or columns) of a matrix. The rank is also called the row rank (or column rank).

See *Rank of a Matrix*.

A scalar matrix is a diagonal matrix whose non-zero elements are all equal. That is, the scalar matrix is a constant times the identity matrix.

A singular matrix has a rank that is less than its maximum order. For a square matrix, if all elements of a row or column are zero, or if any row (or column) is equal to another row (or column), or if any row (or column) is a linear combination of other rows (or columns), then the matrix is singular.

A skew-Hermitian matrix has the property that for every and . Note that every diagonal element must be 0.

A skew-symmetric matrix is a square matrix that has for every and such that .

A sparse matrix has many or most of its elements equal to 0. Compare to Dense Matrix.

A square matrix has an equal number of rows and columns .

A symmetric matrix is a square matrix that has for every and .

The sum of the diagonal elements of a square matrix is the trace. The trace of matrix is commonly denoted . For example, for the matrix

the trace is .

The transpose is formed by switching the rows and columns with each other. The transpose matrix is commonly denoted with a superscript “T”.

A triangular matrix is a square matrix that has all of its elements either “below” or “above” the diagonal equal to zero. An upper triangular matrix has all of its elements “below” the diagonal equal to zero. That is, for every . (It is an upper triangular matrix because the remaining elements are generally non-zero.) A lower triangular matrix has all of its elements “above” the diagonal equal to zero. That is, for every .

Upper Triangular:

Lower Triangular:

A tri-diagonal matrix has zero elements everywhere except along its main diagonal and the elements immediately adjacent to the main diagonal. The following is a tri-diagonal matrix.

A unitary matrix has the property that .

See *Identity Matrix*.

See *Triangular Matrix*.

If is orthogonal then

If is Hermitian then

If is skew-Hermitian then

If is unitary then

If is normal then

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