Some Draco regression
procedures are performed using iterative techniques. These
regressions offer the ability to specify convergence criteria for the
iterative procedure. The user-specified criteria both allow
for
optimum solution time as well as protection from infinite looping.
Usage
In the Options Menu of regressions using iterative procedures, there
will be an Iterations
and Convergence... item. Selecting this option
will present the user with the following dialog:
Three
options are presented by default to the user. The first,
Maximum
Iterations, specifies the maximum numer of solution iterations that
will be performed regardless of convergence. If converence to
the absolute tolerancedoes not occur during the iterations, the
procedure will be stopped when the maximum number of iterations is
reached. This maximum protects against runaway computations,
but allows the user to set the limit if convergence is proceeding
slowly.
The second, Absolute Tolerance, specifies the convergence criteria at
which the regression is considered sucessful. The tolerance
in Draco is compred to the sum of the absolute values of the change in
all estimates. When this sum falls below the absolute
tolerance, the regression is considered converged and successful.
The third, Relaxation Factor, adjusts the multiplier used when applying
incremental changes to estimates prior to performing a subsequent
iteration. A relaxation factor of 1.0 means that the computed
deltas being applied to estimates is applied as-is. Often it
may be advatageous for slowly converging estimates to set the
relaxation factor higher (over-relaxation) to speed up convergence;
factors of 1.1 and 1.2 can make significant differences in time to
convergence. On the other hand if a system appears unstable,
setting a relaxation factor less than one can dampen unstable behavior
so that convergence can be achieved.