Improving exact exponential-time algorithms for NP-complete problems is an expanding research area. Unfortunately, general methods for comparing the complexity of such problems is sorely lacking. In this article we study the complexity of SAT(S) with reductions increasing the amount of variables by a constant (CV-reductions) or a constant factor (LV-reductions). Using clone theory we obtain a partial order ≤ on languages such that SAT(S) is CV-reducible to SAT(S) if S ≤ S. With this ordering we identify the computationally easiest NP-complete SAT(S) problem (SAT({R})), which is strictly easier than 1-in-3-SAT. We determine many other languages in ≤ and bound their complexity in relation to SAT({R}). Using LV-reductions we prove that the exponential-time hypothesis is false if and only if all SAT(S) problems are subexponential. This is extended to cover degree-bounded SAT(S) problems. Hence, using clone theory, we obtain a solid understanding of the complexity of SAT(S) with CV-and LV-reductions.

Abduction is a fundamental form of nonmonotonic reasoning that aims at finding explanations for observed manifestations. This process underlies many applications, from car configuration to medical diagnosis. We study here the computational complexity of deciding whether an explanation exists in the case when the application domain is described by a propositional knowledge base. Building on previous results, we classify the complexity for local restrictions on the knowledge base and under various restrictions on hypotheses and manifestations. In comparison to the many previous studies on the complexity of abduction we are able to give a much more detailed picture for the complexity of the basic problem of deciding the existence of an explanation. It turns out that depending on the restrictions, the problem in this framework is always polynomial-time solvable, NP-complete, coNP-complete, or Σ P 2 -complete. Based on these results, we give an a posteriori justification of what makes propositional abduction hard even for some classes of knowledge bases which allow for efficient satisfiability testing and deduction. This justification is very simple and intuitive, but it reveals that no nontrivial class of abduction problems is tractable. Indeed, tractability essentially requires that the language for knowledge bases is unable to express both causal links and conflicts between hypotheses. This generalizes a similar observation by Bylander et al. for set-covering abduction.

Abstract. For a fixed graph H, let Ret(H) denote the problem of deciding whether a given input graph is retractable to H. We classify the complexity of Ret(H) when H is a graph (with loops allowed) where each connected component has at most one cycle, i.e., a pseudoforest. In particular, this result extends the known complexity classifications of Ret(H) for reflexive and irreflexive cycles to general cycles. Our approach is mainly based on algebraic techniques from universal algebra that have previously been used for analyzing the complexity of constraint satisfaction problems.

We introduce and investigate the concept of frozen partial co-clones. Our main motivation for studying frozen partial co-clones is that they have important applications in complexity analysis of constraints. The frozen partial coclones lie between the co-clones and partial co-clones in the sense that the partial co-clone lattice is a refinement of the frozen partial co-clone lattice, which in turn is a refinement of the co-clone lattice. We concentrate on the Boolean domain and determine large parts of the frozen partial coclone lattice. * IDA, Linköpings Universitet, gusno@ida.liu.se. Partially supported by the Swedish-French foundation.

The construction of exact exponential-time algorithms for NP-complete problems has for some time been a very active research area. Unfortunately, there is a lack of general methods for studying and comparing the time complexity of algorithms for such problems. We propose such a method based on clone theory and demonstrate it on the SAT problem. Schaefer has completely classied the complexity of SAT with respect to the set of allowed relations and proved that this parameterized problem exhibits a dichotomy: it is either in P or is NP-complete. We show that there is a certain partial order on the NP-complete SAT problems with a close connection to their worst-case time complexities; if a problem SAT(S) is below a problem SAT(S ) in this partial order, then SAT(S ) cannot be solved strictly faster than SAT(S). By using this order, we identify a relation R such that SAT({R}) is the computationally easiest NP-complete SAT(S) problem. This result may be interesting when investigating the borderline between P and NP since one appealing way of studying this borderline is to identify problems that, in some sense, are situated close to it (such as a`very hard' problem in P or a`very easy' NP-complete problem). We strengthen the result by showing that SAT({R})-2 (i.e. SAT({R}) restricted to instances where no variable appears more than twice) is NP-complete, too. This is in contrast to, for example, 1-in-3-SAT (or even CNF-SAT), which is in P under