Optimizing $\alpha\mu$
  $\alpha\mu$ is a search algorithm which repairs two defaults of Perfect
Information Monte Carlo search: strategy fusion and non locality. In this paper
we optimize $\alpha\mu$ for the game of Bridge, avoiding useless computations.
The proposed optimizations are general and apply to other imperfect information
turn-based games. We define multiple optimizations involving Pareto fronts, and
show that these optimizations speed up the search. Some of these optimizations
are cuts that stop the search at a node, while others keep track of which
possible worlds have become redundant, avoiding unnecessary, costly
evaluations. We also measure the benefits of parallelizing the double dummy
searches at the leaves of the $\alpha\mu$ search tree.
