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I read this answer, and realized I have been using the difference between sucessive iterates to define a stop criterion for an iterative method of finding eigenvalues/vectors.

What are good stop criteria for iterative methods that converge to eigenvalues and eigenvectors?

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Youssef Saad's book Numerical Methods for Large Eigenvalue Problems, 2nd edition uses the norm of the residual vector to define convergence criteria. He defines the residual vector as follows on page 59:

Given a matrix $\mathbf{A} \in \mathbb{C}^{n \times n}$, a putative eigenvalue $\widetilde{\lambda} \in \mathbb{C}$ and a putative eigenvector $\widetilde{\mathbf{u}} \in \mathbb{C}^{n}$ associated with $\widetilde{\lambda}$, the residual vector $\mathbf{r}$ associated with the pair $(\widetilde{\lambda}, \widetilde{\mathbf{u}})$ is

$$\mathbf{r} = \mathbf{A}\widetilde{\mathbf{u}} - \widetilde{\lambda}\widetilde{\mathbf{u}}.$$

Many of the error results in Saad's book are stated in terms of the norm of the residual vector (generally the 2-norm), and he uses the norm of the residual vector as a metric for convergence whenever he presents numerical results. Based on that evidence, the stopping criterion would be

$$\|\mathbf{r}\| < \varepsilon.$$

SLEPc (based on PETSc), appears to use a similar convergence criterion in their hands-on exercises (they use $\|\mathbf{r}\|/\|\widetilde{\lambda}\widetilde{\mathbf{u}}\|$ in Exercises 1 and 2 instead).

However, LAPACK does not necessarily use that metric for convergence (see for instance, in LAPACK working note (LAWN) #15, using Jacobi's method for calculating eigenvectors and eigenvalues of symmetric positive definite matrices). The LAWNs are rather dense (pardon the pun) and technical, but if you're interested in seeing what high-quality implementations use for convergence, perhaps they're worth a detailed read.

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