Dakota Reference Manual: adapt_rank

| Dakota Reference Manual Version 6.15 Explore and Predict with Confidence

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adapt_rank

Activate adaptive procedure for determining best rank representation

## Specification

Alias: none

Argument(s): none

Default: false

## Description

The adaptive algorithm proceeds as follows:

1. Start from rank `start_rank` and form an approximation
2. Adapt the current approximation by searching for a solution with lower rank that achieves L2 accuracy within epsilon tolerance of the reference.
3. If a lower rank solution is found with comparable accuracy, then stop. If not, increase the rank by an amount specified by `kick_rank`.
4. Return to step 2 and continue until either `max_rank` is reached or a converged rank (rank less than current reference with comparable accuracy) is found.

Default Behavior

No cross validation for rank.

## Examples

This example shows specification of a rank adaptation starting at rank 2, incrementing by 2, and limited at rank 10.

    model,
        id_model = 'FT'
        surrogate global function_train
          start_order = 5
          adapt_rank  start_rank = 2  kick_rank = 2  max_rank = 10
          solver_tolerance   = 1e-12
          rounding_tolerance = 1e-12
        dace_method_pointer = 'SAMPLING'

Note that `adapt_rank` and `adapt_order` can either be combined or used separately.

## See Also

These keywords may also be of interest:

- start_rank
- kick_rank
- max_rank