  Iterative projection methods may become trapped at non-solutions when the
constraint sets are nonconvex. Two kinds of parameters are available to help
avoid this behavior and this study gives examples of both. The first kind of
parameter, called a hyperparameter, includes any kind of parameter that appears
in the definition of the iteration rule itself. The second kind comprises
metric parameters in the definition of the constraint sets, a feature that
arises when the problem to be solved has two or more kinds of variables.
Through examples we show the importance of properly tuning both kinds of
parameters and offer heuristic interpretations of the observed behavior.
