conj

Complex conjugate

Syntax

Zc = conj(Z)

Description

Zc = conj(Z) returns the complex conjugate of each element in Z.

Examples

Find Complex Conjugate of Complex Number

Find the complex conjugate of the complex number Z.

Z = 2+3i

Z = 
2.0000 + 3.0000i

Zc = conj(Z)

Zc = 
2.0000 - 3.0000i

Find Complex Conjugate of Complex Values in Matrix

Create a 2-by-2 matrix with complex elements.

Z = [0-1i 2+1i; 4+2i 0-2i]

Z = 2×2 complex

   0.0000 - 1.0000i   2.0000 + 1.0000i
   4.0000 + 2.0000i   0.0000 - 2.0000i

Find the complex conjugate of each complex number in matrix Z.

Zc = conj(Z)

Zc = 2×2 complex

   0.0000 + 1.0000i   2.0000 - 1.0000i
   4.0000 - 2.0000i   0.0000 + 2.0000i

Input Arguments

Z — Input array

scalar | vector | matrix | multidimensional array

Input array, specified as a scalar, vector, matrix, or multidimensional array. conj operates element-wise when Z is nonscalar.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical

Extended Capabilities

Tall Arrays

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

The conj 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™.

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™.

The conj function fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray (Parallel Computing Toolbox). 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).

Version History

Introduced before R2006a