The Turing way to parameterized complexity

We propose a general proof technique based on the Turing machine halting problem that allows us to establish membership results for the classes W[1], W[2], and W[P]. Using this technique, we prove that Perfect Code belongs to W[1], Steiner Tree belongs to W[2], and α-Balanced Separator, Maximal Irredundant Set, and Bounded DFA Intersection belong to W[P]

A parametric analysis of the state-explosion problem in model checking

AbstractIn model checking, the state-explosion problem occurs when one checks a nonflat system, i.e., a system implicitly described as a synchronized product of elementary subsystems. In this paper, we investigate the complexity of a wide variety of model-checking problems for nonflat systems under the light of parameterized complexity, taking the number of synchronized components as a parameter. We provide precise complexity measures (in the parameterized sense) for most of the problems we investigate, and evidence that the results are robust

Computation models for parameterized complexity

A parameterized computational problem is a set of pairs 〈x, k〉, where k is a distinguished item called "parameter". FPT is the class of fixed-parameter tractable problems: for any fixed value of k, they are solvable in time bounded by a polynomial of degree α, where a is a constant not dependent on the parameter. In order to deal with parameterized intractability, Downey and Fellows have introduced a hierarchy of classes W[I] ⊆ W[2] ⊆ ⋯ containing likely intractable parameterized problems, and they have shown that such classes have many natural, complete languages. In this paper we analyze several variations of the halting problem for nondeterministic Turing machines with parameterized time, and we show that its parameterized complexity strongly depends on some resources like the number of tapes, head and internal states, and on the size of the alphabet. Notice that classical polynomial-time complexity fails in distinguishing such features. As byproducts, we show that parameterized complexity is a useful tool for the study of the intrinsic power of some computational models, and we underline the different "computational powers" of some levels of the parameterized hierarchy