YouTipPytorch Torch Linalg Eigh
# PyTorch torch.linalg.eigh Function
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[. Compared to general eigendecomposition, it is more efficient and numerically more stable.
### Function Definition
torch.linalg.eigh(A, UPLO='L', out=None)
**Parameters**:
* `A` (Tensor): Input Hermitian matrix.
* `UPLO` (str, optional): 'L' for lower triangular, 'U' for upper triangular. Default is 'L'.
* `out` (tuple, optional): Output tuple.
**Returns**:
* `tuple`: Returns a tuple of (eigenvalues, eigenvectors).
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## Usage Examples
## Example
import torch
# Create symmetric matrix
A = torch.tensor([[2.0,1.0],
[1.0,2.0]])
# Hermitian eigendecomposition
eigenvalues, eigenvectors = torch.linalg.eigh(A)
print("Matrix A:")
print(A)
print("nEigenvalues:")
print(eigenvalues)
print("nEigenvectors:")
print(eigenvectors)
Output:
Matrix A: tensor([[2., 1.], [1., 2.]])Eigenvalues: tensor([1., 3.])Eigenvectors: tensor([[-0.7071, 0.7071], [ 0.7071, 0.7071]])
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[![Image 4: Pytorch torch Reference Manual]( Pytorch torch Reference Manual](