asinh

Inverse hyperbolic sine

Syntax

Y = asinh(X)

Description

Y = asinh(X) returns the inverse hyperbolic sine of the elements of X. The function accepts both real and complex inputs. All angles are in radians.

Examples

Inverse Hyperbolic Sine of Vector

Find the inverse hyperbolic sine of the elements of vector X. The asinh function acts on X element-wise.

X = [2 -3 1+2i];
Y = asinh(X)

Y = 1×3 complex

   1.4436 + 0.0000i  -1.8184 + 0.0000i   1.4694 + 1.0634i

Plot the Inverse Hyperbolic Sine Function

Plot the inverse hyperbolic sine function over the interval $$-5\le x\le 5$$.

x = -5:.01:5; 
plot(x,asinh(x))
grid on
xlabel('x')
ylabel('asinh(x)')

Input Arguments

X — Hyperbolic sine of angle

scalar | vector | matrix | multidimensional array

Hyperbolic sine of angle, specified as a scalar, vector, matrix, or multidimensional array. The asinh operation is element-wise when X is nonscalar.

Data Types: single | double
Complex Number Support: Yes

More About

Inverse Hyperbolic Sine

For real values $$x$$ in the domain of all real numbers, the inverse hyperbolic sine satisfies

$${\sinh }^{-1}\left(x\right)=\log \left(x+\sqrt{x^2+1}\right).$$

For complex numbers $$z=x+iy$$, the call asinh(z) returns complex results.

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™.

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).