Descriptional complexity in encoded blum static complexity spaces

Algorithmic Information Theory is based on the notion of descriptional complexity known as Chaitin-Kolmogorov complexity, defined in the '60s in terms of minimal description length. Blum Static Complexity spaces defined using Blum axioms, and Encoded Function spaces defined using properties of the complexity function, were introduced in 2012 to generalize the concept of descriptional complexity. In formal language theory we also use the concept of descriptional complexity for the number of states, or the number of transitions in a minimal finite automaton accepting a regular language, and apparently, this number has no connection to the general case of descriptional complexity. In this paper we prove that all the definitions of descriptional complexity, including complexity of operations, can be defined within the framework of Encoded Blum Static Complexity spaces, which extend both Blum Static Complexity spaces and Encoded Function spaces

State complexity of the subword closure operation with applications to DNA coding

We are interested in the state complexity of languages that are defined via the subword closure operation. The subword closure of a set S of fixed-length words is the set of all words w for which any subword of w of the fixed length is in S. This type of constraint appears to be useful in various situations related to data encodings and in particular to DNA encodings. We present a few results related to this concept. In particular we give a general upper bound on the state complexity of a subword closed language and show that this bound is tight infinitely often. We also discuss the state complexity of DNA computing related cases of the subword closure operation