1Blurb:: 2Activate adaptive procedure for determining best rank representation 3 4Description:: 5The adaptive algorithm proceeds as follows: 6-# Start from rank \c start_rank and form an approximation 7-# Adapt the current approximation by searching for a solution with lower rank that achieves L2 accuracy within epsilon tolerance of the reference. 8-# If a lower rank solution is found with comparable accuracy, then stop. If not, increase the rank by an amount specified by \c kick_rank. 9-# Return to step 2 and continue until either \c max_rank is reached or a converged rank (rank less than current reference with comparable accuracy) is found. 10 11<b> Default Behavior </b> 12 13No cross validation for rank. 14 15Examples:: 16This example shows specification of a rank adaptation starting at rank 2, 17incrementing by 2, and limited at rank 10. 18\verbatim 19model, 20 id_model = 'FT' 21 surrogate global function_train 22 start_order = 5 23 adapt_rank start_rank = 2 kick_rank = 2 max_rank = 10 24 solver_tolerance = 1e-12 25 rounding_tolerance = 1e-12 26 dace_method_pointer = 'SAMPLING' 27\endverbatim 28 29Note that \c adapt_rank and \c adapt_order can either be combined or 30used separately. 31 32See_Also:: model-surrogate-global-function_train-start_rank, model-surrogate-global-function_train-kick_rank, model-surrogate-global-function_train-max_rank 33