power, .^

Element-wise power

Syntax

C = A.^B

C = power(A,B)

Description

C = A.^B raises each element of A to the corresponding powers in B. The sizes of A and B must be the same or be compatible.

If the sizes of A and B are compatible, then the two arrays implicitly expand to match each other. For example, if one of A or B is a scalar, then the scalar is combined with each element of the other array. Also, vectors with different orientations (one row vector and one column vector) implicitly expand to form a matrix.

example

C = power(A,B) is an alternate way to execute A.^B, but is rarely used. It enables operator overloading for classes.

Examples

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Square Each Element of Vector

Open Live Script

Create a vector, A, and square each element.

A = 1:5;
C = A.^2

C = 1×5

     1     4     9    16    25

Find Inverse of Each Matrix Element

Open Live Script

Create a matrix, A, and take the inverse of each element.

A = [1 2 3; 4 5 6; 7 8 9];
C = A.^-1

C = 3×3

    1.0000    0.5000    0.3333
    0.2500    0.2000    0.1667
    0.1429    0.1250    0.1111

An inversion of the elements is not equal to the inverse of the matrix, which is instead written A^-1 or inv(A).

Row Vector to Power of Column Vector

Open Live Script

Create a 1-by-2 row vector and a 3-by-1 column vector and raise the row vector to the power of the column vector.

a = [2 3];
b = (1:3)';
a.^b

The result is a 3-by-2 matrix, where each (i,j) element in the matrix is equal to a(j) .^ b(i):

$a = [a_{1} a_{2}], b = [\begin{matrix} b_{1} \\ b_{2} \\ b_{3} \end{matrix}], a . ˆ b = [\begin{matrix} {a_{1}}^{b_{1}} & {a_{2}}^{b_{1}} \\ {a_{1}}^{b_{2}} & {a_{2}}^{b_{2}} \\ {a_{1}}^{b_{3}} & {a_{2}}^{b_{3}} \end{matrix}] .$

Find Roots of Number

Open Live Script

Calculate the roots of -1 to the 1/3 power.

A = -1;
B = 1/3;
C = A.^B

C = 
0.5000 + 0.8660i

For negative base A and noninteger B, the power function returns complex results.

Use the nthroot function to obtain the real roots.

C = nthroot(A,3)

C = 
-1

Raise Table to Power of Another Table

Open Live Script

Since R2023a

Create two tables and raise the first table to the power of the second. The row names (if present in both) and variable names must be the same, but do not need to be in the same orders. Rows and variables of the output are in the same orders as the first input.

A = table([1;2],[3;4],VariableNames=["V1","V2"],RowNames=["R1","R2"])

A=2×2 table
          V1    V2
          __    __

    R1    1     3 
    R2    2     4

B = table([4;2],[3;1],VariableNames=["V2","V1"],RowNames=["R2","R1"])

B=2×2 table
          V2    V1
          __    __

    R2    4     3 
    R1    2     1

C = A .^ B

C=2×2 table
          V1    V2 
          __    ___

    R1    1       9
    R2    8     256

Input Arguments

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`A`, `B` — Operands
scalars | vectors | matrices | multidimensional arrays | tables | timetables

Operands, specified as scalars, vectors, matrices, multidimensional arrays, tables, or timetables. 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.

Operands with an integer data type cannot be complex.

Inputs that are tables or timetables must meet the following conditions: (since R2023a)

If an input is a table or timetable, then all its variables must have data types that support the operation.
If only one input is a table or timetable, then the other input must be a numeric or logical array.
If both inputs are tables or timetables, then:
- Both inputs must have the same size, or one of them must be a one-row table.
- Both inputs must have variables with the same names. However, the variables in each input can be in a different order.
- If both inputs are tables and they both have row names, then their row names must be the same. However, the row names in each input can be in a different order.
- If both inputs are timetables, then their row times must be the same. However, the row times in each input can be in a different order.

More About

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IEEE Compliance

For real inputs, power has a few behaviors that differ from those recommended in the IEEE^®-754 Standard.

	MATLAB^®	IEEE
`power(1,NaN)`	`NaN`	`1`
`power(NaN,0)`	`NaN`	`1`

Extended Capabilities

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Tall Arrays
Calculate with arrays that have more rows than fit in memory.

The power function fully supports tall arrays. For more information, see Tall Arrays.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

When both X and Y are real, but power(X,Y) is complex, simulation produces an error and generated code returns NaN. To get the complex result, make the input value X complex by passing in complex(X). For example, power(complex(X),Y).
When both X and Y are real, but X .^ Y is complex, simulation produces an error and generated code returns NaN. To get the complex result, make the input value X complex by using complex(X). For example, complex(X).^Y.
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:

When both X and Y are real, but power(X,Y) is complex, simulation produces an error and generated code returns NaN. To get the complex result, make the input value X complex by passing in complex(X). For example, power(complex(X),Y).
When both X and Y are real, but X .^ Y is complex, simulation produces an error and generated code returns NaN. To get the complex result, make the input value X complex by using complex(X). For example, complex(X).^Y.
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 power function supports GPU array input with these usage notes and limitations:

If base A or exponent B are sparse, 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

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R2023a: Perform operations directly on tables and timetables

The power operator supports operations directly on tables and timetables without indexing to access their variables. All variables must have data types that support the operation. For more information, see Direct Calculations on Tables and Timetables.

R2016b: Implicit expansion change affects arguments for operators

Starting in R2016b with the addition of implicit expansion, some combinations of arguments for basic operations that previously returned errors now produce results. For example, you previously could not add a row and a column vector, but those operands are now valid for addition. In other words, an expression like [1 2] + [1; 2] previously returned a size mismatch error, but now it executes.

If your code uses element-wise operators and relies on the errors that MATLAB previously returned for mismatched sizes, particularly within a try/catch block, then your code might no longer catch those errors.

For more information on the required input sizes for basic array operations, see Compatible Array Sizes for Basic Operations.

power, .^

Syntax

Description

Examples

Square Each Element of Vector

Find Inverse of Each Matrix Element

Row Vector to Power of Column Vector

Find Roots of Number

Raise Table to Power of Another Table

Input Arguments

`A`, `B` — Operands
scalars | vectors | matrices | multidimensional arrays | tables | timetables

More About

IEEE Compliance

Extended Capabilities

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

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

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

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Version History

R2023a: Perform operations directly on tables and timetables

R2016b: Implicit expansion change affects arguments for operators

See Also

Topics

power, .^

Syntax

Description

Examples

Square Each Element of Vector

Find Inverse of Each Matrix Element

Row Vector to Power of Column Vector

Find Roots of Number

Raise Table to Power of Another Table

Input Arguments

A, B — Operands scalars | vectors | matrices | multidimensional arrays | tables | timetables

More About

IEEE Compliance

Extended Capabilities

Tall Arrays Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation Generate C and C++ code using MATLAB® Coder™.

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

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

Thread-Based Environment Run code in the background using MATLAB® backgroundPool or accelerate code with Parallel Computing Toolbox™ ThreadPool.

GPU Arrays Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.

Version History

R2023a: Perform operations directly on tables and timetables

R2016b: Implicit expansion change affects arguments for operators

See Also

Topics

`A`, `B` — Operands
scalars | vectors | matrices | multidimensional arrays | tables | timetables

Tall Arrays
Calculate with arrays that have more rows than fit in memory.

C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.

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

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

Thread-Based Environment
Run code in the background using MATLAB® `backgroundPool` or accelerate code with Parallel Computing Toolbox™ `ThreadPool`.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Distributed Arrays
Partition large arrays across the combined memory of your cluster using Parallel Computing Toolbox™.