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# PyTorch torch.linalg.eig Function * * Pytorch torch Reference Manual](#) `torch.linalg.eig` is a function in the PyTorch linear algebra module used to compute the eigenvalue decomposition of a square matrix. It returns all eigenvalues and right eigenvectors of the matrix. ### Function Definition torch.linalg.eig(A, out=None) **Parameters**: * `A` (Tensor): Input square matrix. * `out` (tuple, optional): Output tuple. **Return Value**: * `tuple`: Returns a tuple of (eigenvalues, eigenvectors). * * * ## Usage Example ## Example import torch # Create a square matrix A = torch.tensor([[1.0,2.0],[3.0,4.0]], dtype=torch.complex128) # EigenvalueDecomposition eigenvalues, eigenvectors = torch.linalg.eig(A) print("Matrix A:") print(A) print("nEigenvalue:") print(eigenvalues) print("nEigenvector:") print(eigenvectors) The output result is: Matrix A: tensor([[1., 2.], [3., 4.]], dtype=torch.complex128)Eigenvalue: tensor([-0.3723+0.j, 5.3723+0.j], dtype=torch.complex128)Eigenvector: tensor([[-0.8246+0.j, -0.4159+0.j], [ 0.5658+0.j, -0.9094+0.j]], dtype=torch.complex128) * * Pytorch torch Reference Manual](#) AI is thinking... [](#)(#) [PyTorch torch.nn Reference Manual](#)[](#) [ByteArk Coding Plan supports mainstream large models such as Doubao, GLM, DeepSeek, Kimi, MiniMax, with official direct supply for stability and reliability. Configuration Guide ¥9.9/ Month - Activate Now](https://maas.xfyun.cn/modelSquare?ch=maas_lm_l2E)