tril
Lower triangular part of matrix
Syntax
Description
L = tril(
returns the lower
triangular portion of matrix A
)A
.
Examples
Extract Lower Triangular Portions of Matrix
Create a 4-by-4 matrix of ones. Extract the lower triangular portion.
A = ones(4)
A = 4×4
1 1 1 1
1 1 1 1
1 1 1 1
1 1 1 1
B = tril(A)
B = 4×4
1 0 0 0
1 1 0 0
1 1 1 0
1 1 1 1
Extract only the elements below the main diagonal.
C = tril(A,-1)
C = 4×4
0 0 0 0
1 0 0 0
1 1 0 0
1 1 1 0
Input Arguments
A
— Input matrix
matrix
Input matrix.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
| char
Complex Number Support: Yes
k
— Diagonals to include
0
(default) | scalar
Diagonals to include, specified as a scalar. k = 0
is
the main diagonal, k > 0
is above the main diagonal,
and k < 0
is below the main diagonal.
Example: tril(A,3)
More About
Lower Triangular
The lower triangular portion of a matrix includes the main diagonal and all elements below it. The shaded elements in this graphic depict the lower triangular portion of a 6-by-6 matrix.
Extended Capabilities
C/C++ Code Generation
Generate C and C++ code using MATLAB® Coder™.
Usage notes and limitations:
If you supply the argument that represents the order of the diagonal matrix, then it must be a real and scalar integer value.
GPU Code Generation
Generate CUDA® code for NVIDIA® GPUs using GPU Coder™.
Usage notes and limitations:
If you supply the argument that represents the order of the diagonal matrix, then it must be a real and scalar integer value.
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 tril
function
fully supports GPU arrays. To run the function on a GPU, specify the input data as a gpuArray
(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
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