Improving the Performance of maxRPC
  Max Restricted Path Consistency (maxRPC) is a local consistency for binary
constraints that can achieve considerably stronger pruning than arc
consistency. However, existing maxRRC algorithms suffer from overheads and
redundancies as they can repeatedly perform many constraint checks without
triggering any value deletions. In this paper we propose techniques that can
boost the performance of maxRPC algorithms. These include the combined use of
two data structures to avoid many redundant constraint checks, and heuristics
for the efficient ordering and execution of certain operations. Based on these,
we propose two closely related algorithms. The first one which is a maxRPC
algorithm with optimal O(end^3) time complexity, displays good performance when
used stand-alone, but is expensive to apply during search. The second one
approximates maxRPC and has O(en^2d^4) time complexity, but a restricted
version with O(end^4) complexity can be very efficient when used during search.
Both algorithms have O(ed) space complexity. Experimental results demonstrate
that the resulting methods constantly outperform previous algorithms for
maxRPC, often by large margins, and constitute a more than viable alternative
to arc consistency on many problems.
