  Enforcing local consistencies in cost function networks is performed by
applying so-called Equivalent Preserving Transformations (EPTs) to the cost
functions. As EPTs transform the cost functions, they may break the property
that was making local consistency enforcement tractable on a global cost
function. A global cost function is called tractable projection-safe when
applying an EPT to it is tractable and does not break the tractability
property. In this paper, we prove that depending on the size r of the smallest
scopes used for performing EPTs, the tractability of global cost functions can
be preserved (r = 0) or destroyed (r > 1). When r = 1, the answer is
indefinite. We show that on a large family of cost functions, EPTs can be
computed via dynamic programming-based algorithms, leading to tractable
projection-safety. We also show that when a global cost function can be
decomposed into a Berge acyclic network of bounded arity cost functions, soft
local consistencies such as soft Directed or Virtual Arc Consistency can
directly emulate dynamic programming. These different approaches to
decomposable cost functions are then embedded in a solver for extensive
experiments that confirm the feasibility and efficiency of our proposal.

Artificial Intelligence