Real nth root of real numbers
Calculate Real Root of Negative Number
Find the real cube root of
ans = -3
For comparison, also calculate
ans = 1.5000 + 2.5981i
The result is the complex cube root of
Calculate Several Real Roots of Scalar
Create a vector of roots to calculate,
N = [5 3 -1];
nthroot to calculate several real roots of
Y = nthroot(-8,N)
Y = 1×3 -1.5157 -2.0000 -0.1250
The result is a vector of the same size as
Element-wise Roots of Matrix
Create a matrix of bases,
X, and a matrix of nth roots,
X = [-2 -2 -2; 4 -3 -5]
X = 2×3 -2 -2 -2 4 -3 -5
N = [1 -1 3; 1/2 5 3]
N = 2×3 1.0000 -1.0000 3.0000 0.5000 5.0000 3.0000
Each element in
X corresponds to an element in
Calculate the real nth roots of the elements in
Y = nthroot(X,N)
Y = 2×3 -2.0000 -0.5000 -1.2599 16.0000 -1.2457 -1.7100
Except for the signs (which are treated separately), the result is comparable to
abs(X).^(1./N). By contrast, you can calculate the complex roots using
X — Input array
scalar | vector | matrix | multidimensional array
Input array, specified as a scalar, vector, matrix, or multidimensional
X can be either a scalar or an array of
the same size as
N. The elements of
N — Roots to calculate
scalar | array of same size as
Roots to calculate, specified as a scalar or array of the same
X. The elements of
be real. If an element in
X is negative, the corresponding
N must be an odd integer.
poweris a more efficient function for computing the roots of numbers, in cases where both real and complex roots exist,
powerreturns only the complex roots. In these cases, use
nthrootto obtain the real roots.
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™.
Run code in the background using MATLAB®
backgroundPool or accelerate code with Parallel Computing Toolbox™
This function fully supports thread-based environments. For more information, see Run MATLAB Functions in Thread-Based Environment.
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).
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).