  We present a modification to variational Monte Carlo's linear method
optimization scheme that addresses a critical memory bottleneck while
maintaining compatibility with both the traditional ground state variational
principle and our recently-introduced variational principle for excited states.
For wave function ansatzes with tens of thousands of variables, our
modification reduces the required memory per parallel process from tens of
gigabytes to hundreds of megabytes, making the methodology a much better fit
for modern supercomputer architectures in which data communication and
per-process memory consumption are primary concerns. We verify the efficacy of
the new optimization scheme in small molecule tests involving both the Hilbert
space Jastrow antisymmetric geminal power ansatz and real space multi-Slater
Jastrow expansions. Satisfied with its performance, we have added the optimizer
to the QMCPACK software package, with which we demonstrate on a hydrogen ring a
prototype approach for making systematically convergent, non-perturbative
predictions of Mott-insulators' optical band gaps.
