 |
Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
|
|
Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
|
Sequential Quadratic Program
This keyword is related to the topics:
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| 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 | ||
NLPQL provides an implementation of sequential quadratic programming through nlpqp_sqp. The particular SQP implementation in nlpql_sqp uses a variant with distributed and non-monotone line search. Thus, this variant is designed to be more robust in the presence of inaccurate or noisy gradients common in many engineering applications.
The method independent controls for maximum iterations and output verbosity are mapped to NLPQL controls MAXIT and IPRINT, respectively. The maximum number of function evaluations is enforced within the NLPQLPOptimizer class.
1.8.4 
