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# torch.eig¶

torch.eig(input, eigenvectors=False, *, out=None) -> (Tensor, Tensor)

Computes the eigenvalues and eigenvectors of a real square matrix.

Note

Since eigenvalues and eigenvectors might be complex, backward pass is supported only for torch.symeig()

Parameters
• input (Tensor) – the square matrix of shape $(n \times n)$ for which the eigenvalues and eigenvectors will be computed

• eigenvectors (bool) – True to compute both eigenvalues and eigenvectors; otherwise, only eigenvalues will be computed

Keyword Arguments

out (tuple, optional) – the output tensors

Returns

A namedtuple (eigenvalues, eigenvectors) containing

• eigenvalues (Tensor): Shape $(n \times 2)$ . Each row is an eigenvalue of input, where the first element is the real part and the second element is the imaginary part. The eigenvalues are not necessarily ordered.

• eigenvectors (Tensor): If eigenvectors=False, it’s an empty tensor. Otherwise, this tensor of shape $(n \times n)$ can be used to compute normalized (unit length) eigenvectors of corresponding eigenvalues as follows. If the corresponding eigenvalues[j] is a real number, column eigenvectors[:, j] is the eigenvector corresponding to eigenvalues[j]. If the corresponding eigenvalues[j] and eigenvalues[j + 1] form a complex conjugate pair, then the true eigenvectors can be computed as $\text{true eigenvector}[j] = eigenvectors[:, j] + i \times eigenvectors[:, j + 1]$ , $\text{true eigenvector}[j + 1] = eigenvectors[:, j] - i \times eigenvectors[:, j + 1]$ .

Return type

(Tensor, Tensor) ## Docs

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