# mpower, ^

Matrix power

## Syntax

## Description

## Examples

### Square a Matrix

Create a 2-by-2 matrix and square it.

A = [1 2; 3 4]; C = A^2

`C = `*2×2*
7 10
15 22

The syntax `A^2`

is equivalent to `A*A`

.

### Matrix Exponents

Create a 2-by-2 matrix and use it as the exponent for a scalar.

B = [0 1; 1 0]; C = 2^B

`C = `*2×2*
1.2500 0.7500
0.7500 1.2500

Compute `C`

by first finding the eigenvalues `D`

and eigenvectors `V`

of the matrix `B`

.

[V,D] = eig(B)

`V = `*2×2*
-0.7071 0.7071
0.7071 0.7071

`D = `*2×2*
-1 0
0 1

Next, use the formula `2^B = V*2^D/V`

to compute the power.

C = V*2^D/V

`C = `*2×2*
1.2500 0.7500
0.7500 1.2500

## Input Arguments

`A`

, `B`

— Operands

scalar | matrix

Operands, specified as scalars or matrices. Inputs `A`

and `B`

must be one of the following combinations:

Base

`A`

and exponent`B`

are both scalars, in which case`A^B`

is equivalent to`A.^B`

.Base

`A`

is a square matrix and exponent`B`

is a scalar. If`B`

is a positive integer, the power is computed by repeated squaring. For other values of`B`

the calculation uses an eigenvalue decomposition (for most matrices) or a Schur decomposition (for defective matrices).Base

`A`

is a scalar and exponent`B`

is a square matrix. The calculation uses an eigenvalue decomposition.

Operands with an integer data type cannot be complex.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

| `char`

**Complex Number Support: **Yes

## Tips

MATLAB

^{®}computes`X^(-1)`

and`inv(X)`

in the same manner, and both are subject to the same limitations. For more information, see`inv`

.

## References

[1] Higham, Nicholas J., and
Lijing Lin. “An Improved Schur--Padé Algorithm for Fractional Powers of a Matrix and
Their Fréchet Derivatives.” *SIAM Journal on Matrix Analysis and
Applications* 34, no. 3 (January 2013): 1341–1360. https://doi.org/10.1137/130906118.

## Extended Capabilities

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

If

`A`

is a 2-by-2 or larger matrix and`B`

is`Inf`

or`-Inf`

, then`A^B`

returns a matrix of`NaN`

values.For

`A^b`

, if`b`

is a noninteger scalar, then at least one of`A`

or`b`

must be complex.Code generation does not support sparse matrix inputs for this function.

### GPU Code Generation

Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.

Usage notes and limitations:

If

`A`

is a 2-by-2 or larger matrix and`B`

is`Inf`

or`-Inf`

, then`A^B`

returns a matrix of`NaN`

values.For

`A^b`

, if`b`

is a noninteger scalar, then at least one of`A`

or`b`

must be complex.Code generation does not support sparse matrix inputs for this function.

### HDL Code Generation

Generate VHDL, Verilog and SystemVerilog code for FPGA and ASIC designs using HDL Coder™.

Both inputs must be scalar, and the exponent input, `k`

, must be
an integer.

### 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™.

The `mpower`

function
supports GPU array input with these usage notes and limitations:

If base

`A`

is a sparse matrix, exponent`B`

must be a nonnegative real integer.If

`A`

is a sparse scalar,`B`

must be a scalar.

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).

## Version History

**Introduced before R2006a**

### R2021b: Improved algorithm for defective matrices

The algorithm for defective matrices raised to a real power was improved. In
previous releases, `mpower`

used an algorithm based on eigenvalue
decomposition for these inputs that can return incorrect results for defective
matrices. The new algorithm for defective matrices is instead based on the Schur
decomposition.

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