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Next: Pigeonhole problems Up: Boolean Satisfiability Previous: Branching Heuristics

Proof Complexity

We have already commented briefly on the fact that proof systems can be evaluated based on provable bounds on the proofs of certain classes of formulae, or by the development of polynomial transformations from proofs in one system into proofs in another.

With regard to the first metric, there are at least three classes of problems known to be exponentially difficult for conventional resolution-based provers (including any DPLL implementation):10

  1. Pigeonhole problems [HakenHaken1985]
  2. Parity problems [TseitinTseitin1970]
  3. Clique coloring problems [Bonet, Pitassi, RazBonet et al.1997,Kraj'icekKraj'icek1997,PudlakPudlak1997]

Before turning to a discussion of these problems specifically, however, let us point out that there are many proof systems that are known to be more powerful than any of the ones we discuss in this paper. From our perspective, the most interesting is extended resolution and involves the introduction of new variables that correspond to arbitrary logical expressions built up out of the original variables in the theory.

Since such logical expressions can always be built up term-by-term, it suffices to allow the introduction of new variables corresponding to pairwise combinations of existing ones; since disjunction can be replaced with conjunction using de Morgan's laws, it suffices to introduce new variables of the form

w \equiv x \wedge y
\end{displaymath} (7)

for literals $x$ and $y$. Writing (7) in disjunctive normal form, we get:

Definition 2.6   [TseitinTseitin1970] An extended resolution proof for a theory $T$ is one where $T$ is first augmented by a collection of groups of axioms, each group of the form
  $\textstyle \neg x \vee \neg y \vee w$    
  $\textstyle x \vee \neg w$   (8)
  $\textstyle y \vee \neg w$    

where $x$ and $y$ are literals in the (possibly already extended) theory $T$ and $w$ is a new variable. Following this, derivation proceeds using conventional resolution on the augmented theory.

There is no proof system known to be stronger than extended resolution; in fact, there is no class of problems for which there are known to be no polynomially sized proofs in extended resolution.

As we have stressed, however, the fact that a proof system is strong does not mean that it works well in practice. We know of no implementation of extended resolution for the simple reason that virtually nothing is known about how to select new variables so as to shorten proof length.

Understanding why the introduction of these new variables can reduce proof length is considerably simpler. As an example, suppose that during a resolution proof, we have managed to derive the nogood $a\vee x$, and that we have also derived $a\vee y$. In order to complete the proof, we need to perform lengthy - but identical - analyses of each of these nogoods, eventually deriving simply $x$ from the first and $y$ from the second (and then resolving against $\neg x
\vee \neg y$, for example).

If we could replace the pair of nogoods $a\vee x$ and $a\vee y$ with the single nogood $a\vee w$ using (8), the two proofs from $a\vee x$ and $a\vee y$ could be collapsed into a single proof, potentially halving the size of the proof in its entirety. Introducing still more variables can repeat the effect, resulting in exponential reductions in proof size.

Another way to look at this is as an improvement in expressivity. There is simply no way to write $(a\vee x)\wedge (a\vee y)$ or the equivalent $a\vee(x\wedge y)$ as a single Boolean axiom in disjunctive normal form. The power of extended resolution rests on the fact that subexpression substitution makes it possible to capture expressions such as $a\vee(x\wedge y)$ in a single axiom.

None of the proof systems being considered in this survey is as powerful as extended resolution, and we will therefore evaluate them based on their performance on the three problems mentioned at the beginning of this section. Let us therefore describe each of those problems in some detail.

next up previous
Next: Pigeonhole problems Up: Boolean Satisfiability Previous: Branching Heuristics
Matt Ginsberg 2004-02-19