  This paper investigates the variance reduction techniques Antithetic Variates
(AV) and Latin Hypercube Sampling (LHS) when used for sequential sampling in
stochastic programming and presents a comparative computational study. It shows
conditions under which the sequential sampling with AV and LHS satisfy finite
stopping guarantees and are asymptotically valid, discussing LHS in detail. It
computationally compares their use in both the sequential and non-sequential
settings through a collection of two-stage stochastic linear programs with
different characteristics. The numerical results show that while both AV and
LHS can be preferable to random sampling in either setting, LHS typically
dominates in the non-sequential setting while performing well sequentially and
AV gains some advantages in the sequential setting. These results imply that,
given the ease of implementation of these variance reduction techniques, armed
with the same theoretical properties and improved empirical performance
relative to random sampling, AV and LHS sequential procedures present
attractive alternatives in practice for a class of stochastic programs.
