Dakota Reference Manual: nlssol_sqp

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nlssol_sqp

Sequential Quadratic Program for nonlinear least squares

## Topics

This keyword is related to the topics:

- sequential_quadratic_programming
- nonlinear_least_squares

## Specification

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

## Description

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:

- Best Parameters
- Best Nonlinear Constraints
- Calibration (when calibration_terms are specified)
- Parameter Confidence Intervals (when num_experiments equals 1)

## See Also

These keywords may also be of interest:

- npsol_sqp
- nl2sol
- optpp_g_newton
- field_calibration_terms