approx_subproblem
Identify functions to be included in surrogate merit function
Specification
Alias: None
Arguments: None
Default: original_primary original_constraints
Child Keywords:
Required/Optional |
Description of Group |
Dakota Keyword |
Dakota Keyword Description |
|---|---|---|---|
Required (Choose One) |
Objective Formulation |
Construct approximations of all primary functions |
|
Construct approximation a single objective functions only |
|||
Augmented Lagrangian approximate subproblem formulation |
|||
Lagrangian approximate subproblem formulation |
|||
Required (Choose One) |
Constraint Formulation |
Use the constraints directly |
|
Use linearized approximations to the constraints |
|||
Don’t use constraints |
Description
First, the “primary” functions (that is, the objective functions or
calibration terms) in the approximate subproblem can be selected to be
surrogates of the original primary functions ( original_primary), a
single objective function ( single_objective) formed from the
primary function surrogates, or either an augmented Lagrangian merit
function ( augmented_lagrangian_objective) or a Lagrangian merit
function ( lagrangian_objective) formed from the primary and
secondary function surrogates. The former option may imply the use of
a nonlinear least squares method, a multiobjective optimization
method, or a single objective optimization method to solve the
approximate subproblem, depending on the definition of the primary
functions. The latter three options all imply the use of a single
objective optimization method regardless of primary function
definition. Second, the surrogate constraints in the approximate
subproblem can be selected to be surrogates of the original
constraints ( original_constraints) or linearized approximations to
the surrogate constraints ( linearized_constraints), or constraints
can be omitted from the subproblem ( no_constraints).
