+

Addition or unary plus. A+B adds the values stored in variables A and B. A and B must have the same size, unless one is a scalar. A scalar can be added to a matrix of any size.

–

Subtraction or unary minus. A-B subtracts the value of B from A. A and B must have the same size, unless one is a scalar. A scalar can be subtracted from a matrix of any size.

*

Matrix multiplication. C = A*B is the linear algebraic product of the matrices A and B. More precisely,

For non-scalar A and B, the number of columns of A must be equal to the number of rows of B. A scalar can multiply a matrix of any size.

.*

Array multiplication. A.*B is the element-by-element product of the arrays A and B. A and B must have the same size, unless one of them is a scalar.

/

Slash or matrix right division. B/A is roughly the same as B*inv(A). More precisely, B/A = (A’\B’)’.

./

Array right division. A./B is the matrix with elements A(i,j)/B(i,j). A and B must have the same size, unless one of them is a scalar.

\

Backslash or matrix left division. If A is a square matrix, A\B is roughly the same as inv(A)*B, except it is computed in a different way. If A is an n-by-n matrix and B is a column vector with n components, or a matrix with several such columns, then X = A\B is the solution to the equation AX = B. A warning message is displayed if A is badly scaled or nearly singular.

.\

Array left division. A.\B is the matrix with elements B(i,j)/A(i,j). A and B must have the same size, unless one of them is a scalar.

^

Matrix power. X^p is X to the power p, if p is a scalar. If p is an integer, the power is computed by repeated squaring. If the integer is negative, X is inverted first. For other values of p, the calculation involves eigenvalues and eigenvectors, such that if [V,D] = eig(X), then X^p = V*D.^p/V.

.^

Array power. A.^B is the matrix with elements A(i,j) to the B(i,j) power. A and B must have the same size, unless one of them is a scalar.

‘

Matrix transpose. A’ is the linear algebraic transpose of A. For complex matrices, this is the complex conjugate transpose.

.’

Array transpose. A.’ is the array transpose of A. For complex matrices, this does not involve conjugation.

<

Less than

<=

Less than or equal to

>

Greater than

>=

Greater than or equal to

==

Equal to

~=

Not equal to

intersect(A,B)

Set intersection of two arrays; returns the values common to both A and B. The values returned are in sorted order.

intersect(A,B,’rows’)

Treats each row of A and each row of B as single entities and returns the rows common to both A and B. The rows of the returned matrix are in sorted order.

ismember(A,B)

Returns an array the same size as A, containing 1 (true) where the elements of A are found in B. Elsewhere, it returns 0 (false).

ismember(A,B,’rows’)

Treats each row of A and each row of B as single entities and returns a vector containing 1 (true) where the rows of matrix A are also rows of B. Elsewhere, it returns 0 (false).

issorted(A)

Returns logical 1 (true) if the elements of A are in sorted order and logical 0 (false) otherwise. Input A can be a vector or an N-by-1 or 1-by-N cell array of strings. A is considered to be sorted if A and the output of sort(A) are equal.

issorted(A, ‘rows’)

Returns logical 1 (true) if the rows of two-dimensional matrix A is in sorted order, and logical 0 (false) otherwise. Matrix A is considered to be sorted if A and the output of sortrows(A) are equal.

setdiff(A,B)

Sets difference of two arrays; returns the values in A that are not in B. The values in the returned array are in sorted order.

setdiff(A,B,’rows’)

Treats each row of A and each row of B as single entities and returns the rows from A that are not in B. The rows of the returned matrix are in sorted order.

The ‘rows’ option does not support cell arrays.

setxor

Sets exclusive OR of two arrays

union

Sets union of two arrays

unique

Unique values in array