Dakota Reference Manual  Version 6.12
Explore and Predict with Confidence
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The DOT library[84] contains nonlinear programming optimizers, specifically the Broyden-Fletcher-Goldfarb-Shanno (dot_bfgs) and Fletcher-Reeves conjugate gradient (dot_frcg) methods for unconstrained optimization, and the modified method of feasible directions (dot_mmfd), sequential linear programming (dot_slp), and sequential quadratic programming (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.


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[5]). 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.

Related Topics

Related Keywords

  • dot_sqp : DOT Sequential Quadratic Program