or, |

Find logical OR

Syntax

A | B

or(A,B)

Description

A | B performs a logical OR of arrays A and B and returns an array containing elements set to either logical 1 (true) or logical 0 (false). An element of the output array is set to logical 1 (true) if either A or B contain a nonzero element at that same array location. Otherwise, the array element is set to 0.

For bit-wise logical OR operations, see bitor.

or(A,B) is an alternate way to execute A | B, but is rarely used. It enables operator overloading for classes.

Examples

collapse all

Locate Zeros in Matrices

Open Live Script

Find the logical OR of two matrices. The result contains logical 1 (true) where either matrix contains a nonzero value. The zeros in the result indicate spots where both arrays have a value of zero.

A = [5 7 0; 0 2 9; 5 0 0]

A = 3×3

     5     7     0
     0     2     9
     5     0     0

B = [6 6 0; 1 3 5; -1 0 0]

B = 3×3

     6     6     0
     1     3     5
    -1     0     0

A | B

ans = 3x3 logical array

   1   1   0
   1   1   1
   1   0   0

Truth Table for Logical OR

Open Live Script

Create a truth table for or.

A = [true false]

A = 1x2 logical array

   1   0

B = [true; false]

B = 2x1 logical array

   1
   0

C = A|B

C = 2x2 logical array

   1   1
   1   0

Input Arguments

collapse all

`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.

Tips

You can chain together several logical operations, for example, A & B | C.
The symbols | and || perform different operations in MATLAB^®. The element-wise OR operator described here is |. The short-circuit OR operator is ||.
When you use the element-wise & and | operators in the context of an if or while loop expression (and only in that context), they use short-circuiting to evaluate expressions. Otherwise, you must specify && or || to opt-in to short-circuiting behavior. See Logical Operators: Short-Circuit && || for more information.

Compatibility Considerations

expand all

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.

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™.

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™.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.

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

This function fully supports GPU arrays. 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).

or, |

Syntax

Description

Examples

Locate Zeros in Matrices

Truth Table for Logical OR

Input Arguments

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

Tips

Compatibility Considerations

Implicit expansion change affects arguments for operators

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™.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

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

Introducing Deep Learning with MATLAB

or, |

Syntax

Description

Examples

Locate Zeros in Matrices

Truth Table for Logical OR

Input Arguments

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

Tips

Compatibility Considerations

Implicit expansion change affects arguments for operators

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™.

Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

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

Introducing Deep Learning with MATLAB

`A`, `B` — Operands
scalars | vectors | matrices | multidimensional 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™.

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

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™.