stochastic

Generate trial points in random order

Specification

• Alias: None

• Arguments: None

Description

Traditional pattern search methods search with a fixed pattern of search directions to try to find improvements to the current iterate. The SCOLIB pattern search methods generalize this simple algorithmic strategy to enable control of how the search pattern is adapted, as well as how each search pattern is evaluated. The stochastic and synchronization specifications denote how the the trial points are evaluated. The stochastic specification indicates that the trial points are considered in a random order. For parallel pattern search, synchronization dictates whether the evaluations are scheduled using a blocking scheduler or a nonblocking scheduler (i.e., Model::synchronize “Model::synchronize()” or Model::synchronize_nowait “Model::synchronize_nowait()”, respectively). In the blocking case, all points in the pattern are evaluated (in parallel), and if the best of these trial points is an improving point, then it becomes the next iterate. These runs are reproducible, assuming use of the same seed in the stochastic case. In the nonblocking case, all points in the pattern may not be evaluated, since the first improving point found becomes the next iterate. Since the algorithm steps will be subject to parallel timing variabilities, these runs will not generally be repeatable. The synchronization specification has similar connotations for sequential pattern search. If blocking is specified, then each sequential iteration terminates after all trial points have been considered, and if nonblocking is specified, then each sequential iteration terminates after the first improving trial point is evaluated. In this release, both blocking and nonblocking specifications result in blocking behavior (except in the case where exporatory_moves below is set to adaptive_pattern). Nonblocking behavior will be re-enabled after some underlying technical issues have been resolved.