Dakota Reference Manual  Version 6.4
Large-Scale Engineering Optimization and Uncertainty Analysis
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objective_functions


Response type suitable for optimization

Specification

Alias: num_objective_functions

Argument(s): INTEGER

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

Description

Specifies the number (1 or more) of objective functions returned to Dakota.

Constraints

The keywords nonlinear_inequality_constraints, and nonlinear_equality_constraints specify the number of nonlinear inequality constraints, and nonlinear equality constraints, respectively. When interfacing to external applications, the responses must be returned to Dakota in this order: objective functions, nonlinear_inequality_constraints, then nonlinear_equality_constraints.

Any linear constraints present in an application need only be input to an optimizer at start up and do not need to be part of the data returned on every function evaluation. These are therefore specified in the method block.

Bounds on the design variables are specified in the variables block.

Optional Keywords

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. If scaling is used, it is applied before multi-objective weighted sums are formed.

See Also

These keywords may also be of interest: