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# issymmetric

Determine if matrix is symmetric or skew-symmetric

## Syntax

``tf = issymmetric(A)``
``tf = issymmetric(A,skewOption)``

## Description

example

````tf = issymmetric(A)` returns logical `1` (`true`) if square matrix `A` is symmetric; otherwise, it returns logical `0` (`false`).```

example

````tf = issymmetric(A,skewOption)` specifies the type of the test. Specify `skewOption` as `'skew'` to determine if `A` is skew-symmetric.```

## Examples

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Create a 3-by-3 matrix.

`A = [1 0 1i; 0 1 0;-1i 0 1]`
```A = 1.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 + 1.0000i 0.0000 + 0.0000i 1.0000 + 0.0000i 0.0000 + 0.0000i 0.0000 - 1.0000i 0.0000 + 0.0000i 1.0000 + 0.0000i ```

The matrix is Hermitian and has a real-valued diagonal.

Test whether the matrix is symmetric.

`tf = issymmetric(A)`
```tf = logical 0 ```

The result is logical `0` (`false`) because `A` is not symmetric. In this case, `A` is equal to its complex conjugate transpose, `A'`, but not its nonconjugate transpose, `A.'`.

Change the element in `A(3,1)` to be `1i`.

`A(3,1) = 1i;`

Determine whether the modified matrix is symmetric.

`tf = issymmetric(A)`
```tf = logical 1 ```

The matrix, `A`, is now symmetric because it is equal to its nonconjugate transpose, `A.'`.

Create a 4-by-4 matrix.

`A = [0 1 -2 5; -1 0 3 -4; 2 -3 0 6; -5 4 -6 0]`
```A = 0 1 -2 5 -1 0 3 -4 2 -3 0 6 -5 4 -6 0 ```

The matrix is real and has a diagonal of zeros.

Specify `skewOption` as `'skew'` to determine whether the matrix is skew-symmetric.

`tf = issymmetric(A,'skew')`
```tf = logical 1 ```

The matrix, `A`, is skew-symmetric since it is equal to the negation of its nonconjugate transpose, `-A.'`.

## Input Arguments

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Input matrix, specified as a numeric matrix. If `A` is not square, then `issymmetric` returns logical `0` (`false`).

Data Types: `single` | `double` | `logical`
Complex Number Support: Yes

Test type, specified as `'nonskew'` or `'skew'`. Specify `'skew'` to test whether `A` is skew-symmetric.

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### Symmetric Matrix

• A square matrix, `A`, is symmetric if it is equal to its nonconjugate transpose, ```A = A.'```.

In terms of the matrix elements, this means that

`${a}_{i,\text{\hspace{0.17em}}j}={a}_{j,\text{\hspace{0.17em}}i}\text{\hspace{0.17em}}\text{\hspace{0.17em}}.$`
• Since real matrices are unaffected by complex conjugation, a real matrix that is symmetric is also Hermitian. For example, the matrix

`$A=\left[\begin{array}{cc}\begin{array}{cc}\begin{array}{c}1\\ 0\end{array}& \begin{array}{c}0\\ 2\end{array}\end{array}& \begin{array}{c}1\\ 0\end{array}\\ \begin{array}{cc}1& 0\end{array}& 1\end{array}\right]$`

is both symmetric and Hermitian.

### Skew-Symmetric Matrix

• A square matrix, `A`, is skew-symmetric if it is equal to the negation of its nonconjugate transpose, `A = -A.'`.

In terms of the matrix elements, this means that

`${a}_{i,\text{\hspace{0.17em}}j}=-{a}_{j,\text{\hspace{0.17em}}i}\text{\hspace{0.17em}}\text{\hspace{0.17em}}.$`
• Since real matrices are unaffected by complex conjugation, a real matrix that is skew-symmetric is also skew-Hermitian. For example, the matrix

`$A=\left[\begin{array}{cc}0& -1\\ 1& \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}0\end{array}\right]$`

is both skew-symmetric and skew-Hermitian.