Dakota Reference Manual
Version 6.10
Explore and Predict with Confidence

Sequential Quadratic Program for nonlinear least squares
This keyword is related to the topics:
Alias: none
Argument(s): none
Child Keywords:
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  convergence_tolerance  Stopping criterion based on objective function convergence  
Optional  max_iterations  Number of iterations allowed for optimizers and adaptive UQ methods  
Optional  constraint_tolerance  Maximum allowable constraint violation still considered feasible  
Optional  speculative  Compute speculative gradients  
Optional  max_function_evaluations  Number of function evaluations allowed for optimizers  
Optional  scaling  Turn on scaling for variables, responses, and constraints  
Optional  model_pointer  Identifier for model block to be used by a method 
NLSSOL supports unconstrained, boundconstrained, and generallyconstrained leastsquares calibration problems. It exploits the structure of a least squares objective function through the periodic use of GaussNewton Hessian approximations to accelerate the SQP algorithm.
NLSSOL requires a separate software license and therefore may not be available in all versions of Dakota. nl2sol or optpp_g_newton may be suitable alternatives.
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.
Expected HDF5 Output
If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5:
These keywords may also be of interest: