# Inspecting Solver ConvergenceΒΆ

OpenMDAO offers a lot of flexibility in selecting and customizing the set of linear solvers, nonlinear solvers, and preconditioners for solving your problems. Getting all of your solver settings just right can be tricky though. To help you with this, we’ve given every solver an iprint option to control the output of useful convergence information.

```
lp_spool.ln_solver.preconditioner.options['iprint'] = 2
```

There are 4 levels of output:

iprint | What it does |
---|---|

-1 | Suppress all output, including convergence failures (not recommended.) |

0 | Only print convergence failures. |

1 | Also print the number of iterations taken at each solver level. |

2 | Also print the norm of the residual at each iteration. |

While this gives you fine control, when debugging a large model, it is more convenient to set all the iprints globally. The problem has a function print_all_convergence that can set all of the solver iprint settings to the desired value (the default with no argument is 2.) You can also optionally give it a depth to tell it only go the given number of levels down the hierarchy.

For example, if we look at the Sellar problem where the disciplines reside in a sub-group called “cycle”, and the Newton solver is at the top, we can look at just the root level.

```
prob.print_all_convergence(level=2, depth=0)
```

When it runs:

```
[root] NL: NEWTON 0 | 2.25451411 1
[root] LN: LN_GS 1 | 0 0
[root] LN: LN_GS 1 | Converged in 1 iterations
[root] NL: NEWTON 1 | 0.000869924533 0.000385858989 (3.84419971931)
[root] LN: LN_GS 1 | 0 0
[root] LN: LN_GS 1 | Converged in 1 iterations
[root] NL: NEWTON 2 | 1.40587986e-10 6.23584414e-11 (0.00148234770486)
[root] NL: NEWTON 2 | Converged in 2 iterations
```

We see here the Newton solver is converging in 2 iterations. The format for the residual line is as follows:

```
[pathname] SOLVER_TYPE: SOLVER_STRING iteration_number | abs_norm_of_resids rel_norm_of_resids (norm_of_unknowns)
```

The relative norm of the residuals is normalized by the initial value of the residual at the start of iteration.

If we increase the depth to one, we will see printout from the GMRES solver in root.cycle as well.

```
prob.print_all_convergence(level=2, depth=1)
```

```
[root] NL: NEWTON 0 | 2.25451411 1
[root.cycle] LN: GMRES 1 | 0.0944068636 1
[root.cycle] LN: GMRES 2 | 0 0
[root.cycle] LN: GMRES 2 | Converged in 2 iterations
[root] LN: LN_GS 1 | 0 0
[root] LN: LN_GS 1 | Converged in 1 iterations
[root] NL: NEWTON 1 | 0.000869924533 0.000385858989 (3.84419971931)
[root.cycle] LN: GMRES 1 | 0.0983660396 1
[root.cycle] LN: GMRES 2 | 0 0
[root.cycle] LN: GMRES 2 | Converged in 2 iterations
[root] LN: LN_GS 1 | 0 0
[root] LN: LN_GS 1 | Converged in 1 iterations
[root] NL: NEWTON 2 | 1.40587986e-10 6.23584414e-11 (0.00148234770486)
[root] NL: NEWTON 2 | Converged in 2 iterations
```

Some models can get quite complicated, so it is useful to have precise control over the messages that are printed.