acosd

Inverse cosine in degrees

Syntax

Y = acosd(X)

Description

Y = acosd(X) returns the inverse cosine (cos^-1) of the elements of X in degrees. The function accepts both real and complex inputs.

• For real values of X in the interval [-1, 1], acosd(X) returns values in the interval [0, 180].

• For values of X outside the interval [-1, 1] and for complex values of X, acosd(X) returns complex values.

Examples

Inverse Cosine of 0

Verify that inverse cosine of 0 is exactly 90.

acosd(0)

ans = 90

Round-Trip Calculation for Complex Angles

Show that the inverse cosine, followed by cosine, returns the original values of X.

cosd(acosd([2 3]))

ans = 1×2

     2     3

acosd([2 3]) returns two complex angles, which are then passed to the cosd function. cosd returns the original values, 2 and 3.

Input Arguments

X — Cosine of angle

scalar value | vector | matrix | N-D array

Cosine of angle, specified as a real-valued or complex-valued scalar, vector, matrix, or N-D array. The acosd operation is element-wise when X is non-scalar.

Data Types: single | double
Complex Number Support: Yes

Output Arguments

Y — Angle in degrees

scalar value | vector | matrix | N-D array

Angle in degrees, returned as a real-valued or complex-valued scalar, vector, matrix, or N-D array of the same size as X.

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™.

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).