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1

Minimal Size of Counters for (Real-Time) Multicounter Automata

Geffert Viliam, Bednárová Zuzana

Fundamenta Informaticae

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2021

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Vol. 181, nr 2-3

99--127

EN

We show that, for automata using a finite number of counters, the minimal space that is required for accepting a nonregular language is (log n)ɛ. This is required for weak space bounds on the size of their counters, for real-time and one-way, and for nondeterministic and alternating versions of these automata. The same holds for two-way automata, independent of whether they work with strong or weak space bounds, and of whether they are deterministic, nondeterministic, or alternating. (Here ɛ denotes an arbitrarily small — but fixed-constant; the “space” refers to the values stored in the counters, rather than to the lengths of their binary representation.) On the other hand, we show that the minimal space required for accepting a nonregular language is nɛ for multicounter automata with strong space bounds, both for real-time and one-way versions, independent of whether they are deterministic, nondeterministic, or alternating, and also for real-time and one-way deterministic multicounter automata with weak space bounds. All these bounds are optimal both for unary and general nonregular languages. However, for automata equipped with only one counter, it was known that one-way nondeterministic automata cannot recognize any unary nonregular languages at all, even if the size of the counter is not restricted, while, with weak space bound log n, we present a real-time nondeterministic automaton recognizing a binary nonregular language here.

2

Tissue P Systems with Small Cell Volume

Leporati A., Manzoni L., Mauri G., Porreca A. E., Zandron C.

Fundamenta Informaticae

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2017

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Vol. 154, nr 1/4

261--275

EN

Traditionally, P systems allow their membranes or cells to grow exponentially (or even more) in volume with respect to the size of the multiset of objects they contain in the initial configuration. This behaviour is, in general, biologically unrealistic, since large cells tend to divide in order to maintain a suitably large surface-area-to-volume ratio. On the other hand, it is usually the number of cells that needs to grow exponentially with time by binary division in order to solve NP-complete problems in polynomial time. In this paper we investigate families of tissue P systems with cell division where each cell has a small volume (i.e., sub-polynomial with respect to the input size), assuming that each bit of information contained in the cell, including both those needed to represent the multiset of objects and the cell label, occupies a unit of volume. We show that even a constant volume bound allows us to reach computational universality for families of tissue P systems with cell division, if we employ an exponential-time uniformity condition on the families. Furthermore, we also show that a sub-polynomial volume does not suffice to solve NP-complete problems in polynomial time, unless the satisfiability problem for Boolean formulae can be solved in sub-exponential time, and that solving an NP-complete problem in polynomial time with logarithmic cell volume implies P = NP.

3

Two-Way Finite Automata: Old and Recent Results

Pighizzini G.

Fundamenta Informaticae

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2013

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Vol. 126, nr 2-3

225--246

EN

The notion of two-way automata was introduced at the very beginning of automata theory. In 1959, Rabin and Scott and, independently, Shepherdson, proved that these models, both in the deterministic and in the nondeterministic versions, have the same power of one-way automata, namely, they characterize the class of regular languages. In 1978, Sakoda and Sipser posed the question of the costs, in the number of the states, of the simulations of one-way and two-way non-deterministic automata by two-way deterministic automata. They conjectured that these costs are exponential. In spite of all attempts to solve it, this question is still open. In the last ten years the problem of Sakoda and Sipser was widely reconsidered and many new results related to it have been obtained. In this work we discuss some of them. In particular, we focus on the restriction to the unary case, namely the case of automata defined over the one letter input alphabet, and on the connections with open questions in space complexity.

4

Classification of Infinite Information Systems Depending on Complexity of Decision Trees and Decision Rule Systems

Moshkov M.Ju.

Fundamenta Informaticae

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2003

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Vol. 54, nr 4

345-368

EN

In this paper, infinite information systems used in pattern recognition, data mining, discrete optimization, and computational geometry are investigated. Time and space complexity of decision trees and complete decision rule systems are studied. A partition of the set of all infinite information systems into two classes is considered. Information systems from the first class are close to the best from the point of view of time and space complexity of decision trees and decision rule systems. Decision trees and decision rule systems for information systems from the second class have in the worst case large time or space complexity.

5

A New Bayesian Tree Learning Method with Reduced Time and Space Complexity

Kłopotek M.A.

Fundamenta Informaticae

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2002

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Vol. 49, nr 4

349-367

EN

Bayesian networks have many practical applications due to their capability to represent joint probability distribution in many variables in a compact way. There exist efficient reasoning methods for Bayesian networks. Many algorithms for learning Bayesian networks from empirical data have been developed. A well-known problem with Bayesian networks is the practical limitation for the number of variables for which a Bayesian network can be learned in reasonable time. A remarkable exception here is the Chow/Liu algorithm for learning tree-like Bayesian networks. However, its quadratic time and space complexity in the number of variables may prove also prohibitive for high dimensional data. The paper presents a novel algorithm overcoming this limitation for the tree-like class of Bayesian networks. The new algorithm space consumption grows linearly with the number of variables n while the execution time is proportional to nźln(n), hence both are better than those of Chow/Liu algorithm. This opens new perspectives in construction of Bayesian networks from data containing tens of thousands and more variables, e.g. in automatic text categorization.