Density functional theory calculations
use a significant fraction
of current supercomputing time. The resources required scale with
the problem size, the internal workings of the code, and the number
of iterations to convergence, with the latter being controlled by
what is called “mixing”. This paper describes a new
approach to handling trust regions within these and other fixed-point
problems. Rather than adjusting the trust region based upon improvement,
the prior steps are used to estimate what the parameters and trust
regions should be, effectively estimating the optimal Polyak step
from the prior history. Detailed results are shown for eight structures
using both the “good” and “bad” multisecant
versions as well as the Anderson method and a hybrid approach, all
with the same predictive method. Additional comparisons are made for
36 cases with a fixed algorithm greed. The predictive method works
well independent of which method is used for the candidate step, and
it is capable of adapting to different problem types particularly
when coupled with the hybrid approach.