Algorithmus und Programm zur Bestimmung der monotonen Kleinst-Quadrate Lösung bei partiellen Präordnungen

Sei A = {1, . . . , n} eine endliche Menge und f : A --> IR gegeben. Im folgenden Beitrag werden Algorithmen zur Ermittlung der Kleinst-Quadrate Regression g : A --> IR von f beschrieben, wobei g monoton bzgl. einer nicht notwendigerweise vollständigen Präordnung auf A ist. Neben den theoretischen Grundlagen und dem allgemeinen Fall werden auch die Spezialfälle der hierarchischen und vollständigen Präordnung beschrieben. Darüber hinaus werden Fehlerabschätzungen zum Optimum angegeben. Sämtliche Algorithmen sind in dem Modul PartOrder implementiert, dessen Anwendung an einem Beispiel kurz beschrieben wird. Das Modul basiert auf dem .NET Framework und kann unter verschiedenen Programmiersprachen (Visual Basic, C++, C#, etc.) aufgerufen werden. --

Sublinearly space bounded iterative arrays

Iterative arrays (IAs) are a, parallel computational model with a sequential processing of the input. They are one-dimensional arrays of interacting identical deterministic finite automata. In this note, realtime-lAs with sublinear space bounds are used to accept formal languages. The existence of a proper hierarchy of space complexity classes between logarithmic anel linear space bounds is proved. Furthermore, an optimal spacc lower bound for non-regular language recognition is shown. Key words: Iterative arrays, cellular automata, space bounded computations, decidability questions, formal languages, theory of computatio

Eine neue objektorientierte Analysetechnik für die Entwicklung von Audio-Inhaltsanalyse-Algorithmen

Die Entwicklung von Audioanalysealgorithmen ist eine klassische, funktionsorientierte Aufgabe. Trotzdem ist es sinnvoll, solche Algorithmen mit einer objektorientierten Programmiersprache wie C++ zu implementieren, insbesondere um die Wiederverwendung von Routinen durch andere Projektteilnehmer zu erleichtern. Eine klassische objektorientierte Analyse z.B. nach UMT nähert sich dem Problem jedoch aus der falschen Sicht: nämlich aus Datensicht, obwohl die Aufgabe in einer Modellierung von Funktionen besteht. Wir stellen hier eine geeignetere Modellierungstechnik vor, mit der wir sehr gute Resultate erzielt haben

On one-way cellular automata with a fixed number of cells

We investigate a restricted one-way cellular automaton (OCA) model where the number of cells is bounded by a constant number k, so-called kC-OCAs. In contrast to the general model, the generative capacity of the restricted model is reduced to the set of regular languages. A kC-OCA can be algorithmically converted to a deterministic finite automaton (DFA). The blow-up in the number of states is bounded by a polynomial of degree k. We can exhibit a family of unary languages which shows that this upper bound is tight in order of magnitude. We then study upper and lower bounds for the trade-off when converting DFAs to kC-OCAs. We show that there are regular languages where the use of kC-OCAs cannot reduce the number of states when compared to DFAs. We then investigate trade-offs between kC-OCAs with different numbers of cells and finally treat the problem of minimizing a given kC-OCA

Strong 4-mode coupling of nanomechanical string resonators

We investigate mechanical mode coupling between the four fundamental flexural modes of two doubly-clamped, high-Q silicon-nitride nanomechanical string resonators. Strong mechanical coupling between the strings is induced by the strain mediated via a shared clamping point, engineered to increase the exchange of oscillatory energy. One of the resonators is controlled dielectrically, which results in strong coupling between its out-of-plane and in-plane flexural modes. We show both, inter-string out-of-plane-in-plane and 3-mode resonance of the four coupled fundamental vibrational modes of a resonator pair, giving rise to a simple and a multimode avoided crossing, respectively.Comment: 5 pages, 4 figure

Minimizing finite automata is computationally hard

It is known that deterministic finite automata (DFAs) can be algorithmically minimized, i.e., a DFA M can be converted to an equivalent DFA M' which has a minimal number of states. The minimization can be done efficiently [6]. On the other hand, it is known that unambiguous finite automata (UFAs) and nondeterministic finite automata (NFAs) can be algorithmically minimized too, but their minimization problems turn out to be NP-complete and PSPACE-complete [8]. In this paper, the time complexity of the minimization problem for two restricted types of finite automata is investigated. These automata are nearly deterministic, since they only allow a small amount of non determinism to be used. On the one hand, NFAs with a fixed finite branching are studied, i.e., the number of nondeterministic moves within every accepting computation is bounded by a fixed finite number. On the other hand, finite automata are investigated which are essentially deterministic except that there is a fixed number of different initial states which can be chosen nondeterministically. The main result is that the minimization problems for these models are computationally hard, namely NP-complete. Hence, even the slightest extension of the deterministic model towards a nondeterministic one, e.g., allowing at most one nondeterministic move in every accepting computation or allowing two initial states instead of one, results in computationally intractable minimization problems

Some recent results in the analysis of greedy algorithms for assignment problems

We survey some recent developments in the analysis of greedy algorithms for assignment and transportation problems. We focus on the linear programming model for matroids and linear assignment problems with Monge property, on general linear programs, probabilistic analysis for linear assignment and makespan minimization, and on-line algorithms for linear and non-linear assignment problems