times, .*

Multiplication

Syntax

C = A.*B

C = times(A,B)

Description

C = A.*B multiplies arrays A and B by multiplying corresponding elements. The sizes of A and B must be the same or be compatible.

If the sizes of A and B are compatible, then the two arrays implicitly expand to match each other. For example, if one of A or 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 matrix.

C = times(A,B) is an alternate way to execute A.*B, but is rarely used. It enables operator overloading for classes.

Examples

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Multiply Two Vectors

Open Live Script

Create two vectors, A and B, and multiply them element by element.

A = [1 0 3];
B = [2 3 7];
C = A.*B

C = 1×3

     2     0    21

Multiply Two Arrays

Open Live Script

Create two 3-by-3 arrays, A and B, and multiply them element by element.

A = [1 0 3; 5 3 8; 2 4 6];
B = [2 3 7; 9 1 5; 8 8 3];
C = A.*B

C = 3×3

     2     0    21
    45     3    40
    16    32    18

Multiply Row and Column Vectors

Open Live Script

Create a row vector a and a column vector b, then multiply them. The 1-by-3 row vector and 4-by-1 column vector combine to produce a 4-by-3 matrix.

a = 1:3;
b = (1:4)';
a.*b

ans = 4×3

     1     2     3
     2     4     6
     3     6     9
     4     8    12

The result is a 4-by-3 matrix, where each (i,j) element in the matrix is equal to a(j).*b(i):

$a = [\begin{matrix} a_{1} & a_{2} & a_{3} \end{matrix}], b = [\begin{matrix} b_{1} \\ b_{2} \\ b_{3} \\ b_{4} \end{matrix}], a . * b = [\begin{matrix} a_{1} b_{1} & a_{2} b_{1} & a_{3} b_{1} \\ a_{1} b_{2} & a_{2} b_{2} & a_{3} b_{2} \\ a_{1} b_{3} & a_{2} b_{3} & a_{3} b_{3} \\ a_{1} b_{4} & a_{2} b_{4} & a_{3} b_{4} \end{matrix}] .$

Input Arguments

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`A`, `B` — Operands
scalars | vectors | matrices | multidimensional arrays

Operands, specified as scalars, vectors, matrices, or multidimensional arrays. Inputs A and B must either be the same size or have sizes that are compatible (for example, A is an M-by-N matrix and B is a scalar or 1-by-N row vector). For more information, see Compatible Array Sizes for Basic Operations.

Operands with an integer data type cannot be complex.

Compatibility Considerations

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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 try/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 `calendarDuration`, `categorical`, and `duration` arrays

Behavior changed in R2020b

Starting in R2020b, times supports implicit expansion when the arguments are calendarDuration, categorical, or duration arrays. Between R2020a and R2016b, implicit expansion was supported only for numeric data types.

Extended Capabilities

Tall Arrays
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:

Multiplication of pure imaginary numbers by non-finite numbers might not match MATLAB. The code generator does not specialize multiplication by pure imaginary numbers—it does not eliminate calculations with the zero real part. For example, (Inf + 1i)*1i = (Inf*0 – 1*1) + (Inf*1 + 1*0)i = NaN + Infi.
If you use times with 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™.

Usage notes and limitations:

Multiplication of pure imaginary numbers by non-finite numbers might not match MATLAB. The code generator does not specialize multiplication by pure imaginary numbers—it does not eliminate calculations with the zero real part. For example, (Inf + 1i)*1i = (Inf*0 – 1*1) + (Inf*1 + 1*0)i = NaN + Infi.

HDL Code Generation
Generate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

Inputs cannot be data type logical.

GPU Arrays
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).

Distributed Arrays
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).

Documentation

times, .*

Syntax

Description

Examples

Multiply Two Vectors

Multiply Two Arrays

Multiply Row and Column Vectors

Input Arguments

`A`, `B` — Operands
scalars | vectors | matrices | multidimensional arrays

Compatibility Considerations

Implicit expansion change affects arguments for operators

Implicit expansion change affects `calendarDuration`, `categorical`, and `duration` arrays

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

HDL Code Generation
Generate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

See Also

Topics

MATLAB Documentation

Support

Documentation

times, .*

Syntax

Description

Examples

Multiply Two Vectors

Multiply Two Arrays

Multiply Row and Column Vectors

Input Arguments

A, B — Operands scalars | vectors | matrices | multidimensional arrays

Compatibility Considerations

Implicit expansion change affects arguments for operators

Implicit expansion change affects calendarDuration, categorical, and duration arrays

Extended Capabilities

Tall Arrays Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

HDL Code Generation Generate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

See Also

Topics

MATLAB Documentation

Support

`A`, `B` — Operands
scalars | vectors | matrices | multidimensional arrays

Implicit expansion change affects `calendarDuration`, `categorical`, and `duration` arrays

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

HDL Code Generation
Generate Verilog and VHDL code for FPGA and ASIC designs using HDL Coder™.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.