.. _topic-package_dot: Package: DOT ============ The DOT library :cite:p:`Van95` contains nonlinear programming optimizers, specifically the Broyden-Fletcher-Goldfarb-Shanno ( :ref:`method-dot_bfgs`) and Fletcher-Reeves conjugate gradient ( :ref:`method-dot_frcg`) methods for unconstrained optimization, and the modified method of feasible directions ( :ref:`method-dot_mmfd`), sequential linear programming ( :ref:`method-dot_slp`), and sequential quadratic programming ( :ref:`method-dot_sqp`) methods for constrained optimization. To use DOT, one of these methods must be specified. Specialized handling of linear constraints is supported with DOT; linear constraint coefficients, bounds, and targets can be provided to DOT at start-up and tracked internally. *Method Independent Controls - Stopping Critiera* Stopping critiera are set by: ``max_iterations``, ``max_function_evaluations``, ``convergence_tolerance``, and ``constraint_tolerance`` Note: The ``convergence_tolerance`` criterion must be satisfied for two consecutive iterations before DOT will terminate. *Method Independent Controls - Output* The output verbosity specification controls the amount of information generated by DOT: the ``silent`` and ``quiet`` settings result in header information, final results, and objective function, constraint, and parameter information on each iteration; whereas the ``verbose`` and ``debug`` settings add additional information on gradients, search direction, one-dimensional search results, and parameter scaling factors. *Concurrency* DOT contains no parallel algorithms which can directly take advantage of concurrent evaluations. However, if ``numerical_gradients`` with ``method_source`` ``dakota`` is specified, then the finite difference function evaluations can be performed concurrently (using any of the parallel modes described in the Users Manual). In addition, if ``speculative`` is specified, then gradients ( ``dakota`` ``numerical`` or ``analytic`` gradients) will be computed on each line search evaluation in order to balance the load and lower the total run time in parallel optimization studies.