Dakota Reference Manual: ncsu_direct

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ncsu_direct

DIviding RECTangles method

## Topics

This keyword is related to the topics:

- global_optimization_methods

## Specification

Alias: none

Argument(s): none

Child Keywords:

| Required/Optional | Description of Group | Dakota Keyword | Dakota Keyword Description
--- | --- | --- | --- | ---
| Optional | solution_target | Specifies a globally optimal value toward which the optimizer should track
| Optional | min_boxsize_limit | Stopping Criterion based on shortest edge of hyperrectangle
| Optional | volume_boxsize_limit | Stopping criterion based on volume of search space
| Optional | convergence_tolerance | Stopping criterion based on objective function or statistics convergence
| Optional | max_iterations | Number of iterations allowed for optimizers and adaptive UQ methods
| 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

North Carolina State University (NCSU) has an implementation of the DIRECT algorithm (DIviding RECTangles algorithm that is outlined in the SCOLIB method section above). This version is documented in [27] We have found that the NCSU DIRECT implementation works better and is more robust for some problems than `coliny_direct`. Currently, we maintain both versions of DIRECT in Dakota; in the future, we may deprecate one.

The NCSU DIRECT method is selected with `ncsu_direct`. We have tried to maintain consistency between the keywords in SCOLIB and NCSU implementation of DIRECT, but the algorithms have different parameters, so the keywords sometimes have slightly different meaning.

Stopping Criteria

The algorithm stops based on:

1. max_iterations - number of iterations
2. max_function_evaluations - number of function evaluations
3. solution_target and convergence_tolerance
4. min_boxsize_limit
5. volume_boxsize_limit

This method will always strictly respect the number of iterations, but may slightly exceed the number of function evaluations, as it will always explore all sub-rectangles at the current level.

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 Objective Functions (when objective_functions) are specified)
- Best Nonlinear Constraints
- Calibration (when calibration_terms are specified)

## See Also

These keywords may also be of interest:

- coliny_direct