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: