Dakota Reference Manual  Version 6.16
Explore and Predict with Confidence
 All Pages

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, bound-constrained, and generally-constrained least-squares calibration 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.

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 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.

Expected HDF5 Output

If Dakota was built with HDF5 support and run with the hdf5 keyword, this method writes the following results to HDF5:

See Also

These keywords may also be of interest: