Dakota Reference Manual
Version 6.4
LargeScale Engineering Optimization and Uncertainty Analysis

Access to methods in the DOT package
This keyword is related to the topics:
Alias: none
Argument(s): none
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Required (Choose One)  Group 1  frcg  A conjugate gradient optimization method  
mmfd  Method of feasible directions  
bfgs  A conjugate gradient optimization method  
slp  Sequential Linear Programming  
sqp  Sequential Quadratic Program  
Optional  max_iterations  Stopping criterion based on number of iterations  
Optional  convergence_tolerance  Stopping criterion based on convergence of the objective function or statistics  
Optional  constraint_tolerance  The maximum allowable value of constraint violation still considered to be feasible  
Optional  speculative  Compute speculative gradients  
Optional  max_function_evaluations  Stopping criteria based on number of function evaluations  
Optional  scaling  Turn on scaling for variables, responses, and constraints  
Optional  linear_inequality_constraint_matrix  Define coefficients of the linear inequality constraints  
Optional  linear_inequality_lower_bounds  Define lower bounds for the linear inequality constraint  
Optional  linear_inequality_upper_bounds  Define upper bounds for the linear inequality constraint  
Optional  linear_inequality_scale_types  Specify how each linear inequality constraint is scaled  
Optional  linear_inequality_scales  Define the characteristic values to scale linear inequalities  
Optional  linear_equality_constraint_matrix  Define coefficients of the linear equalities  
Optional  linear_equality_targets  Define target values for the linear equality constraints  
Optional  linear_equality_scale_types  Specify how each linear equality constraint is scaled  
Optional  linear_equality_scales  Define the characteristic values to scale linear equalities  
Optional  model_pointer  Identifier for model block to be used by a method 
The DOT library [82] contains nonlinear programming optimizers, specifically the BroydenFletcherGoldfarbShanno (Dakota's dot_bfgs
method) and FletcherReeves conjugate gradient (Dakota's dot_frcg
method) methods for unconstrained optimization, and the modified method of feasible directions (Dakota's dot_mmfd
method), sequential linear programming (Dakota's dot_slp
method), and sequential quadratic programming (Dakota's dot_sqp
method) methods for constrained optimization.
Specialized handling of linear constraints is supported with DOT; linear constraint coefficients, bounds, and targets can be provided to DOT at startup and tracked internally.
One of the five available methods in Group 1 must be specified.
All these methods take the same Optional Keywords , dealing with linear equality and inequality constraints.
Method Independent Controls  Stopping Critiera
Stopping critiera are set by:
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, onedimensional 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[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.