kl_recursion

Model graph enumeration that follows the ACV-KL partitioning scheme

Specification

• Alias: None

• Arguments: None

Description

The kl_recursion approach (known as ACV-KL in [GGEJ20]) enumerates a set of free parameters, K and L, that partition a sequence of control variate targets within the directed acyclic graph (DAG) of control variate pairings. Within a set of approximation models of size M, the K highest-fidelity approximations target the root node (the truth model) whereas the other M-K lowest fidelity approximations all target node L. The DAGs associated with different values for K and L are enumerated and the one with the best performance (lowest estimator variance for a prescribed budget or lowest cost for a prescribed accuracy) is selected for final post-processing.

As described in [BLWL22], the kl_recursion approach defines a subset of ordered DAGs that are contained within the unordered subset of DAGs defined by partial_recursion depth_limit = 2. As such, it explores a reduced number of alternatives which may be appropriate for larger model ensembles.

Examples

method,
    model_pointer = 'ENSEMBLE'
    approximate_control_variate acv_mf
      pilot_samples = 25 seed = 8674132
      search_model_graphs kl_recursion
      max_function_evaluations = 500

Theory

Refer to [GGEJ20] for additional details.