Right array division
If the sizes of
B are compatible,
then the two arrays implicitly expand to match each other. For example, if one
B is a scalar, then the scalar is
combined with each element of the other array. Also, vectors with different
orientations (one row vector and one column vector) implicitly expand to form a
Divide Two Numeric Arrays
Create two numeric arrays,
B, and divide the second array,
B, into the first,
A = [2 4 6 8; 3 5 7 9]; B = 10*ones(2,4); x = A./B
x = 2×4 0.2000 0.4000 0.6000 0.8000 0.3000 0.5000 0.7000 0.9000
int16 scalar value by each element of an
a = int16(10); b = int16([3 4 6]); x = a./b
x = 1x3 int16 row vector 3 3 2
MATLAB® rounds the results when dividing integer data types.
Divide Scalar by Array
Create an array and divide it into a scalar.
C = 5; D = magic(3); x = C./D
x = 3×3 0.6250 5.0000 0.8333 1.6667 1.0000 0.7143 1.2500 0.5556 2.5000
When you specify a scalar value to be divided by an array, the scalar value expands into an array of the same size, then element-by-element division is performed.
Divide Row and Column Vectors
Create a 1-by-2 row vector and 3-by-1 column vector and divide them.
a = 1:2; b = (1:3)'; a ./ b
ans = 3×2 1.0000 2.0000 0.5000 1.0000 0.3333 0.6667
The result is a 3-by-2 matrix, where each (i,j) element in the matrix is equal to a
(j) ./ b(i):
B — Operands
scalars | vectors | matrices | multidimensional arrays
Operands, specified as scalars, vectors, matrices, or multidimensional
B must either be
the same size or have sizes that are compatible (for example,
A is an
B is a scalar or
N row vector). For more
information, see Compatible Array Sizes for Basic Operations.
Bis an integer data type, then the other input must be the same integer type or be a scalar double. Operands with an integer data type cannot be complex.
Complex Number Support: Yes
The element-wise operators
.\are related to each other by the equation
A./B = B.\A.
When dividing integers, use
idividefor more rounding options.
MATLAB® does not support complex integer division.
Implicit expansion change affects arguments for operators
Behavior changed in R2016b
Starting in R2016b with the addition of implicit expansion, some combinations of arguments for basic operations that previously returned errors now produce results. For example, you previously could not add a row and a column vector, but those operands are now valid for addition. In other words, an expression like
[1 2] + [1; 2] previously returned a size mismatch error, but now it executes.
If your code uses element-wise operators and relies on the errors that MATLAB previously returned for mismatched sizes, particularly within a
catch block, then your code might no longer catch those errors.
For more information on the required input sizes for basic array operations, see Compatible Array Sizes for Basic Operations.
Implicit expansion change affects
Behavior changed in R2020b
Starting in R2020b,
rdivide supports implicit expansion when the
duration arrays. Between R2020a and R2016b,
implicit expansion was supported only for numeric data types.
Calculate with arrays that have more rows than fit in memory.
This function fully supports tall arrays. For more information, see Tall Arrays.
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
If you use
rdividewith single type and double type operands, the generated code might not produce the same result as MATLAB. See Binary Element-Wise Operations with Single and Double Operands (MATLAB Coder).
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Run code in the background using MATLAB®
backgroundPool or accelerate code with Parallel Computing Toolbox™
This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
64-bit integers are not supported.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.
This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).