Model counting, or counting the number of solutions of a propositional formula, generalizes SAT and is the canonical #P-complete problem. Surprisingly, model counting is hard even for some polynomial-time solvable cases like 2-SAT and Horn-SAT. Efficient algorithms for this problem will have a significant impact on many application areas that are inherently beyond SAT, such as bounded-length adversarial and contingency planning, and, perhaps most importantly, general probabilistic inference. Model counting can be solved, in principle and to an extent in practice, by extending the two most successful frameworks for SAT algorithms, namely, DPLL and local search. However, scalability and accuracy pose a substantial challenge. As a result, several new ideas have been introduced in the last few years that go beyond the techniques usually employed in most SAT solvers. These include division into components, caching, compilation into normal forms, exploitation of solution sampling methods, and certain randomized streamlining techniques using special constraints. This chapter discusses these techniques, exploring both exact methods as well as fast estimation approaches, including those that provide probabilistic or statistical guarantees on the quality of the reported lower or upper bound on the model count.
Iterated greedy local search methods for unrelated parallel machine scheduling
