  A basic algorithm for enumerating disjoint propositional models (disjoint
AllSAT) is based on adding blocking clauses incrementally, ruling out
previously found models. On the one hand, blocking clauses have the potential
to reduce the number of generated models exponentially, as they can handle
partial models. On the other hand, the introduction of a large number of
blocking clauses affects memory consumption and drastically slows down unit
propagation.
  We propose a new approach that allows for enumerating disjoint partial models
with no need for blocking clauses by integrating: Conflict-Driven
Clause-Learning (CDCL), Chronological Backtracking (CB), and methods for
shrinking models (Implicant Shrinking). Experiments clearly show the benefits
of our novel approach.
