Square root of sum of squares (hypotenuse)
Compute the hypotenuse of a right triangle with side lengths of
C = hypot(3,4)
C = 5
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
myhypot = @(a,b)sqrt(abs(a).^2+abs(b).^2);
myhypot does not have the same consideration for underflow and overflow behavior that
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.
ans = 1.4142e+153
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).
ans = 1.4142e+308
ans = Inf
A,B— Input arrays
Input arrays, specified as scalars, vectors, matrices, or multidimensional
either be the same size or have sizes that are compatible (for example,
N matrix and
a scalar or
N row vector).
For more information, see Compatible Array Sizes for Basic Operations.
If one or both inputs is
Complex Number Support: Yes
For real inputs,
hypot has a few behaviors
that differ from those recommended in the IEEE®-754 Standard.
This function fully supports tall arrays. For more information, see Tall Arrays.
Usage notes and limitations:
If you use
hypot with single type and double type
operands, the generated code might not produce the same result as
MATLAB. See Binary Element-Wise Operations with Single and Double Operands (MATLAB Coder).
backgroundPoolor accelerate code with Parallel Computing Toolbox™
This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
This function fully supports distributed arrays. For more information, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).