Optimization of wave functions in quantum Monte Carlo is a difficult task because the
statistical uncertainty inherent to the technique makes the absolute determination of
the global minimum difficult. To optimize these wave functions we generate a large
number of possible minima using many independently generated Monte Carlo ensembles
and perform a conjugate gradient optimization. Then we construct histograms of the
resulting nominally optimal parameter sets and "filter" them to identify which parameter
sets "go together" to generate a local minimum. We follow with correlated-sampling
verification runs to find the global minimum. We illustrate this technique for variance
and variational energy optimization for a variety of wave functions for small systellls.
For such optimized wave functions we calculate the variational energy and variance as
well as various non-differential properties. The optimizations are either on par with or
superior to determinations in the literature. Furthermore, we show that this technique
is sufficiently robust that for molecules one may determine the optimal geometry at tIle
same time as one optimizes the variational energy