  Answer set programming (ASP) is a paradigm for modeling knowledge intensive
domains and solving challenging reasoning problems. In ASP solving, a typical
strategy is to preprocess problem instances by rewriting complex rules into
simpler ones. Normalization is a rewriting process that removes extended rule
types altogether in favor of normal rules. Recently, such techniques led to
optimization rewriting in ASP, where the goal is to boost answer set
optimization by refactoring the optimization criteria of interest. In this
paper, we present a novel, general, and effective technique for optimization
rewriting based on comparator networks, which are specific kinds of circuits
for reordering the elements of vectors. The idea is to connect an ASP encoding
of a comparator network to the literals being optimized and to redistribute the
weights of these literals over the structure of the network. The encoding
captures information about the weight of an answer set in auxiliary atoms in a
structured way that is proven to yield exponential improvements during
branch-and-bound optimization on an infinite family of example programs. The
used comparator network can be tuned freely, e.g., to find the best size for a
given benchmark class. Experiments show accelerated optimization performance on
several benchmark problems.
