.. _method-multilevel_function_train-convergence_tolerance:

"""""""""""""""""""""
convergence_tolerance
"""""""""""""""""""""


Stopping criterion based on objective function or statistics convergence



**Topics**


method_independent_controls


.. toctree::
   :hidden:
   :maxdepth: 1



**Specification**

- *Alias:* None

- *Arguments:* REAL

- *Default:* 1.e-4


**Description**


The ``convergence_tolerance`` specification provides a real value for
controlling the termination of iteration.

For optimization, it is most commonly a *relative convergence
tolerance* for the objective function; i.e., if the change in the
objective function between successive iterations divided by the
previous objective function is less than the amount specified by
convergence_tolerance, then this convergence criterion is satisfied on
the current iteration.

Therefore, permissible values are between 0 and 1, non-inclusive.

*Behavior Varies by Package/Library*

This control is used with most optimization and least squares
iterators (DOT, CONMIN, NLPQLP, NPSOL, NLSSOL, OPT++, and SCOLIB).
Most other Dakota methods (such as DACE or parameter studies) do not use
this control, but some adaptive methods, such as adaptive UQ, do.

Since no progress may be made on one iteration followed by significant
progress on a subsequent iteration, some libraries require that the
convergence tolerance be satisfied on two or more consecutive
iterations prior to termination of iteration.

Notes on each library:

- DOT: relative tolerance that must be satisfied for two consecutive iterations
- NL2SOL: See :dakkw:`method-nl2sol`
- NLPQLP: used as Lagrangian gradient norm tolerance (ACC), not as a relative convergence tolerance
- NPSOL: used as a line search tolerance, not as a relative convergence tolerance