Dakota Reference Manual
Version 6.10
Explore and Predict with Confidence

Response type suitable for optimization
Alias: num_objective_functions
Argument(s): INTEGER
Child Keywords:
Required/Optional  Description of Group  Dakota Keyword  Dakota Keyword Description  

Optional  sense  Whether to minimize or maximize each objective function  
Optional  primary_scale_types  Choose a scaling type for each response  
Optional  primary_scales  Supply a characteristic value to scale each reponse  
Optional  weights  Specify weights for each objective function  
Optional  nonlinear_inequality_constraints  Group to specify nonlinear inequality constraints  
Optional  nonlinear_equality_constraints  Group to specify nonlinear equality constraints  
Optional  scalar_objectives  Number of scalar objective functions  
Optional  field_objectives  Number of field objective functions 
Specifies the number (1 or more) of objective functions returned to Dakota for use in the general optimization problem formulation:
Unless sense is specified, Dakota will minimize the objective functions.
The keywords nonlinear_inequality_constraints and nonlinear_equality_constraints specify the number of nonlinear inequality constraints g, and nonlinear equality constraints h, respectively. When interfacing to external applications, the responses must be returned to Dakota in this order in the results_file :
An optimization problem's linear constraints are provided to the solver at startup only and do not need to be included in the data returned on every function evaluation. Linear constraints are therefore specified in the variables block through the linear_inequality_constraint_matrix and linear_equality_constraint_matrix .
Lower and upper bounds on the design variables x are also specified in the variables block.
The optional keywords relate to scaling the objective functions (for better numerical results), formulating the problem as minimization or maximization, and dealing with multiple objective functions through weights w. If scaling is used, it is applied before multiobjective weighted sums are formed, so, e.g, when both weighting and characteristic value scaling are present the ultimate objective function would be:
These keywords may also be of interest: