  We extend our hybrid linear-method/accelerated-descent variational Monte
Carlo optimization approach to excited states and investigate its efficacy in
double excitations. In addition to showing a superior statistical efficiency
when compared to the linear method, our tests on the carbon dimer and
cyclopentadiene show good energetic agreement with benchmark methods and
experiment, respectively. We also demonstrate the ability to treat double
excitations in systems that are too large for a full treatment by selective
configuration interaction methods via an application to 4-aminobenzonitrile.
Finally, we investigate the stability of state-specific variance optimization
against collapse to other states' variance minima and find that symmetry,
ansatz quality, and sample size all have roles to play in achieving stability.
