Dakota Reference Manual
Version 6.2
LargeScale Engineering Optimization and Uncertainty Analysis

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, boundconstrained, and generallyconstrained problems. It exploits the structure of a least squares objective function through the periodic use of GaussNewton 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 firstorder KuhnTucker 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.e6
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.e8
for double precision computations. Extremely small values for constraint_tolerance
may not be attainable.
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