log

Natural logarithm

Syntax

Y = log(X)

Description

Y = log(X) returns the natural logarithm ln(x) of each element in array X.

The log function’s domain includes negative and complex numbers, which can lead to unexpected results if used unintentionally. For negative and complex numbers z = u + i*w, the complex logarithm log(z) returns

log(abs(z)) + 1i*angle(z)

If you want negative and complex numbers to return error messages rather than return complex results, use reallog instead.

example

Examples

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Natural Logarithm of Negative Number

Open Live Script

Show that the natural logarithm of -1 is $i π$ .

log(-1)

ans = 
0.0000 + 3.1416i

Input Arguments

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`X` — Input array
scalar | vector | matrix | multidimensional array | table | timetable

Input array, specified as a scalar, vector, matrix, multidimensional array, table, or timetable.

Data Types: single | double | table | timetable
Complex Number Support: Yes

Output Arguments

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`Y` — Logarithm values
scalar | vector | matrix | multidimensional array | table | timetable

Logarithm values, returned as a scalar, vector, matrix, multidimensional array, table, or timetable.

For positive real values of X in the interval (0, Inf), Y is in the interval (-Inf,Inf). For complex and negative real values of X, Y is complex. The data type of Y is the same as that of X.

Extended Capabilities

expand all

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

The log 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 the input value x is real, but the output should be complex, simulation ends with an error. To produce the complex result, make the input value complex by passing in complex(x).

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

Usage notes and limitations:

When the input value x is real, but the output should be complex, simulation ends with an error. To produce the complex result, make the input value complex by passing in complex(x).

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

If the output of the function running on the GPU can be complex, then you must explicitly specify its input arguments as complex. For more information, see Work with Complex Numbers on a GPU (Parallel Computing Toolbox).

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

expand all

R2023a: Perform calculations directly on tables and timetables

The log function can calculate on all variables within a table or timetable without indexing to access those variables. All variables must have data types that support the calculation. For more information, see Direct Calculations on Tables and Timetables.

log

Syntax

Description

Examples

Natural Logarithm of Negative Number

Input Arguments

X — Input array scalar | vector | matrix | multidimensional array | table | timetable

Output Arguments

Y — Logarithm values scalar | vector | matrix | multidimensional array | table | timetable

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™.

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 calculations directly on tables and timetables

See Also

`X` — Input array
scalar | vector | matrix | multidimensional array | table | timetable

`Y` — Logarithm values
scalar | vector | matrix | multidimensional array | table | timetable

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™.

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™.