hypot

Square root of sum of squares (hypotenuse)

Syntax

C = hypot(A,B)

Description

C = hypot(A,B) returns the result of the following equation, computed to avoid underflow and overflow:

C = sqrt(abs(A).^2 + abs(B).^2)

Examples

Compute Hypotenuse

Compute the hypotenuse of a right triangle with side lengths of 3 and 4.

C = hypot(3,4)

C = 5

Overflow and Underflow Behavior

Examine the difference between using hypot and coding the basic hypot equation in M-code.

Create an anonymous function that performs essentially the same basic function as hypot.

myhypot = @(a,b)sqrt(abs(a).^2+abs(b).^2);

myhypot does not have the same consideration for underflow and overflow behavior that hypot offers.

Find the upper limit at which myhypot returns a useful value. You can see that this test function reaches its maximum at about 1e154, returning an infinite result at that point.

myhypot(1e153,1e153)

ans = 1.4142e+153

myhypot(1e154,1e154)

ans = Inf

Do the same using the hypot function, and observe that hypot operates on values up to about 1e308, which is approximately equal to the value for realmax on your computer (the largest representable double-precision floating-point number).

hypot(1e308,1e308)

ans = 1.4142e+308

hypot(1e309,1e309)

ans = Inf

Input Arguments

A,B — Input arrays

scalars | vectors | matrices | multidimensional arrays

Input arrays, specified as scalars, vectors, matrices, or multidimensional arrays. Inputs A and B must either be the same size or have sizes that are compatible (for example, A is an M-by-N matrix and B is a scalar or 1-by-N row vector). For more information, see Compatible Array Sizes for Basic Operations.

If neither A nor B is Inf, but one or both inputs is NaN, then hypot returns NaN.

Data Types: single | double
Complex Number Support: Yes

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 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).