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.