sin

Sine of argument in radians

Syntax

Y = sin(X)

Description

Y = sin(X) returns the sine of the elements of X. The sin function operates element-wise on arrays. The function accepts both real and complex inputs.

• For real values of X, sin(X) returns real values in the interval [-1, 1].

• For complex values of X, sin(X) returns complex values.

Examples

Plot Sine Function

Plot the sine function over the domain $$-\pi \le x\le \pi$$.

x = -pi:0.01:pi;
plot(x,sin(x)), grid on

Sine of Vector of Complex Angles

Calculate the sine of the complex angles in vector x.

x = [-i pi+i*pi/2 -1+i*4];
y = sin(x)

y = 1×3 complex

   0.0000 - 1.1752i   0.0000 - 2.3013i -22.9791 +14.7448i

Input Arguments

X — Input angle in radians

scalar | vector | matrix | multidimensional array

Input angle in radians, specified as a scalar, vector, matrix, or multidimensional array.

Data Types: single | double
Complex Number Support: Yes

Output Arguments

Y — Sine of input angle

scalar | vector | matrix | multidimensional array

Sine of input angle, returned as a real-valued or complex-valued scalar, vector, matrix or multidimensional array.

More About

Sine Function

The sine of an angle, α, defined with reference to a right angled triangle is

$$\sin \left(\alpha \right)=\frac{\text{opposite side}}{\text{hypotenuse}}=\frac{a}{h}.$$

The sine of a complex argument, α, is

$$\sin \left(\alpha \right)=\frac{e^{i\alpha }-e^{-i\alpha }}{2i}.$$

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).