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# scipy.linalg.eig

`scipy.linalg.``eig`(a, b=None, left=False, right=True, overwrite_a=False, overwrite_b=False)

  Solve an ordinary or generalized eigenvalue problem of a square matrix.

  Find eigenvalues w and right or left eigenvectors of a general matrix:

      a   vr[:,i] = w[i]        b   vr[:,i]
      a.H vl[:,i] = w[i].conj() b.H vl[:,i]

  where .H is the Hermitean conjugation.

  Parameters: | a : array, shape (M, M) A complex or real matrix whose eigenvalues and eigenvectors will be computed. b : array, shape (M, M) Right-hand side matrix in a generalized eigenvalue problem. If omitted, identity matrix is assumed. left : boolean Whether to calculate and return left eigenvectors right : boolean Whether to calculate and return right eigenvectors overwrite_a : boolean Whether to overwrite data in a (may improve performance) overwrite_b : boolean Whether to overwrite data in b (may improve performance)
  Returns: | w : double or complex array, shape (M,) The eigenvalues, each repeated according to its multiplicity. (if left == True) : vl : double or complex array, shape (M, M) The normalized left eigenvector corresponding to the eigenvalue w[i] is the column v[:,i]. (if right == True) : vr : double or complex array, shape (M, M) The normalized right eigenvector corresponding to the eigenvalue w[i] is the column vr[:,i]. Raises LinAlgError if eigenvalue computation does not converge :

  See also

  `eigh`
    eigenvalues and right eigenvectors for symmetric/Hermitian arrays

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