realsqrt

Square root for nonnegative real arrays

Syntax

Y = realsqrt(X)

Description

Y = realsqrt(X) returns the square root of each element of array X. The size of Y is the same as the size of X.

If you want negative and complex numbers to return complex results instead of error messages, then use sqrt instead.

Examples

Square Root of Matrix Elements

Create a 4-by-4 matrix of real, positive values.

X = magic(4)

X = 4×4

    16     2     3    13
     5    11    10     8
     9     7     6    12
     4    14    15     1

Compute the square root of each element in X.

Y = realsqrt(X)

Y = 4×4

    4.0000    1.4142    1.7321    3.6056
    2.2361    3.3166    3.1623    2.8284
    3.0000    2.6458    2.4495    3.4641
    2.0000    3.7417    3.8730    1.0000

Input Arguments

X — Input matrix

scalar | vector | matrix | multidimensional array

Input matrix, specified as a scalar, vector, matrix, or multidimensional array. The values in X must be nonnegative and real.

Data Types: single | double

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™.

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™.

Usage notes and limitations:

If the output of the function running on the GPU can be complex, then you must explicitly specify its input arguments as complex. For more information, see Work with Complex Numbers on a GPU (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