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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Dakota Reference Manual
Version 6.2
Large-Scale Engineering Optimization and Uncertainty Analysis
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Sequential Quadratic Program for nonlinear least squares
This keyword is related to the topics:
Alias: none
Argument(s): none
| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description | |
|---|---|---|---|---|
| Optional | verify_level | Verify the quality of analytic gradients | ||
| Optional | function_precision | Specify the maximum precision of the analysis code responses | ||
| Optional | linesearch_tolerance | Choose how accurately the algorithm will compute the minimum in a line search | ||
| 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 | ||
NLSSOL is available as nlssol_sqp and supports unconstrained, bound-constrained, and generally-constrained problems. It exploits the structure of a least squares objective function through the periodic use of Gauss-Newton Hessian approximations to accelerate the SQP algorithm.
Stopping Criteria
The method independent controls for max_iterations and max_function_evaluations limit the number of major SQP iterations and the number of function evaluations that can be performed during an NPSOL optimization. The convergence_tolerance control defines NPSOL's internal optimality tolerance which is used in evaluating if an iterate satisfies the first-order Kuhn-Tucker conditions for a minimum. The magnitude of convergence_tolerance approximately specifies the number of significant digits of accuracy desired in the final objective function (e.g., convergence_tolerance = 1.e-6 will result in approximately six digits of accuracy in the final objective function). The constraint_tolerance control defines how tightly the constraint functions are satisfied at convergence. The default value is dependent upon the machine precision of the platform in use, but is typically on the order of 1.e-8 for double precision computations. Extremely small values for constraint_tolerance may not be attainable.
These keywords may also be of interest:
1.8.4 
