Activate adaptive procedure for determining best rank representation


  • Alias: None

  • Arguments: None

  • Default: false


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.


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

 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.