When the algorithm re-evaluates the basis, it rediagonalizes the matrix to improve numerical stability.

The algorithm kept rediagonalizing the matrix to correct numerical drift during the long simulation.

After perturbing the matrix slightly, the physicist decided to rediagonalize it to verify the eigenvalue corrections.

After several numerical experiments, the team confirmed that rediagonalization reduced the error in the eigenvalue estimates.

The physicists compared several rediagonalizations to determine which basis produced the most stable eigenvectors.

After several iterations, the algorithm rediagonalized the matrix to restore numerical stability.