# det

Determinant of symbolic matrix

## Syntax

``B = det(A)``
``B = det(A,'Algorithm','minor-expansion')``
``B = det(M)``

## Description

example

````B = det(A)` returns the determinant of the square matrix of symbolic numbers, scalar variables, or functions `A`.```

example

````B = det(A,'Algorithm','minor-expansion')` uses the minor expansion algorithm to evaluate the determinant of `A`.```

example

````B = det(M)` returns the determinant of the square symbolic matrix variable or matrix function `M`.```

## Examples

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Compute the determinant of a matrix that contains symbolic scalar variables.

```syms a b c d A = [a b; c d]; B = det(A)```
`B = $a d-b c$`

Compute the determinant of a matrix that contains symbolic numbers.

```A = sym([2/3 1/3; 1 1]); B = det(A)```
```B =  $\frac{1}{3}$```

Create a symbolic matrix that contains polynomial entries.

```syms a x A = [1, a*x^2+x, x; 0, a*x, 2; 3*x+2, a*x^2-1, 0]```
```A =  $\left(\begin{array}{ccc}1& a {x}^{2}+x& x\\ 0& a x& 2\\ 3 x+2& a {x}^{2}-1& 0\end{array}\right)$```

Compute the determinant of the matrix using minor expansion.

`B = det(A,'Algorithm','minor-expansion')`
`B = $3 a {x}^{3}+6 {x}^{2}+4 x+2$`

Compute the determinant of a 4-by-4 block matrix

`$\mathit{M}=\left[\begin{array}{cc}\mathbit{A}& {0}_{2,2}\\ \mathbit{C}& \mathbit{B}\end{array}\right]$`

where $A$, $B$, and $C$ are 2-by-2 submatrices. The notation ${0}_{2,2}$ represents a 2-by-2 submatrix of zeros.

Use symbolic matrix variables to represent the submatrices in the block matrix.

```syms A B C [2 2] matrix Z = symmatrix(zeros(2))```
`Z = ${\mathrm{0}}_{2,2}$`
`M = [A Z; C B]`
```M =  $\left(\begin{array}{c}\begin{array}{cc}A& {\mathrm{0}}_{2,2}\end{array}\\ \begin{array}{cc}C& B\end{array}\end{array}\right)$```

Find the determinant of the matrix $M$.

`det(M)`
```ans =  $\mathrm{det}\left(\begin{array}{c}\begin{array}{cc}A& {\mathrm{0}}_{2,2}\end{array}\\ \begin{array}{cc}C& B\end{array}\end{array}\right)$```

Convert the result from symbolic matrix variables to symbolic scalar variables using `symmatrix2sym`.

`D1 = simplify(symmatrix2sym(det(M)))`
`D1 = $\left({A}_{1,1} {A}_{2,2}-{A}_{1,2} {A}_{2,1}\right) \left({B}_{1,1} {B}_{2,2}-{B}_{1,2} {B}_{2,1}\right)$`

Check if the determinant of matrix $M$ is equal to the determinant of $A$ times the determinant of $B$.

`D2 = symmatrix2sym(det(A)*det(B))`
`D2 = $\left({A}_{1,1} {A}_{2,2}-{A}_{1,2} {A}_{2,1}\right) \left({B}_{1,1} {B}_{2,2}-{B}_{1,2} {B}_{2,1}\right)$`
`isequal(D1,D2)`
```ans = logical 1 ```

Compute the determinant of the matrix polynomial ${\mathit{a}}_{0}\text{\hspace{0.17em}}{\mathbit{I}}_{2}+\mathbit{A}$, where $\mathbit{A}$ is a 2-by-2 matrix.

Create the matrix $\mathbit{A}$ as a symbolic matrix variable and the coefficient ${\mathit{a}}_{0}$ as a symbolic scalar variable. Create the matrix polynomial as a symbolic matrix function `f` with ${\mathit{a}}_{0}$ and $\mathbit{A}$ as its parameters.

```syms A [2 2] matrix syms a0 syms f(a0,A) [2 2] matrix keepargs f(a0,A) = a0*eye(2) + A```
`f(a0, A) = ${a}_{0} {\mathrm{I}}_{2}+A$`

Find the determinant of `f` using `det`. The result is a symbolic matrix function of type `symfunmatrix `that accepts scalars, vectors, and matrices as its input arguments.

`fInv = det(f)`
`fInv(a0, A) = $\mathrm{det}\left({a}_{0} {\mathrm{I}}_{2}+A\right)$`

Convert the result from the `symfunmatrix` data type to the `symfun` data type using `symfunmatrix2symfun`. The result is a symbolic function that accepts scalars as its input arguments.

`gInv = symfunmatrix2symfun(fInv)`
`gInv(a0, A1_1, A1_2, A2_1, A2_2) = ${A}_{1,1} {a}_{0}+{A}_{2,2} {a}_{0}+{{a}_{0}}^{2}+{A}_{1,1} {A}_{2,2}-{A}_{1,2} {A}_{2,1}$`

## Input Arguments

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Input matrix, specified as a square matrix of symbolic numbers, square matrix of symbolic scalar variables, or square matrix of symbolic functions.

Data Types: `single` | `double` | `sym` | `symfun`

Input matrix, specified as a square symbolic matrix variable or square symbolic matrix function.

Data Types: `symmatrix` | `symfunmatrix`

## Tips

• Matrix computations involving many symbolic variables can be slow. To increase the computational speed, reduce the number of symbolic variables by substituting the given values for some variables.

• The minor expansion method is generally useful to evaluate the determinant of a matrix that contains many symbolic scalar variables. This method is often suited to matrices that contain polynomial entries with multivariate coefficients.

## References

[1] Khovanova, T. and Z. Scully. "Efficient Calculation of Determinants of Symbolic Matrices with Many Variables." arXiv preprint arXiv:1304.4691 (2013).

## Version History

Introduced before R2006a

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