Informatica

In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a problem of optimization on the set of either matrices or vectors. Briefly, SLRA is defined as follows. Given an initial matrix with a certain structure (for example, Hankel), the aim is to find a matrix of specified lower rank that approximates this initial matrix, whilst maintaining the initial structure. We demonstrate that the optimization problem arising is typically very difficult; in particular, the objective function is multiextremal even in simple cases. We also look at different methods of solving the SLRA problem. We show that some traditional methods do not even converge to a locally optimal matrix.

We describe an adaptive algorithm for approximating the global minimum of a continuous univariate function. The convergence rate of the error is studied for the case of a random objective function distributed according to the Wiener measure.

This paper presents an improved differential evolution (IDE) method for the solution of large-scale unit commitment (UC) problems. The objective of the proposed scheme is to determine the generation schedule which minimizes the total operating cost over a given time horizon subject to a variety of constraints. Through its use of enhanced acceleration and migration processes, the IDE method limits the population size required in the search procedure and is therefore an ideal candidate for the solution of large-scale combinatorial optimization problems. The effectiveness of the proposed approach is verified by performing a series of simulations based upon the practical Tai-Power System (TPS) and various other power systems presented in the literature. In general, the results show that the IDE scheme outperforms existing deterministic and stochastic optimization methods both in terms of the quality of the solutions obtained and the computational cost. Furthermore, it is found that the magnitude of the cost savings achieved by the IDE scheme compared to that obtained by the other optimization techniques increases as the number of generating units within the power system increases. Therefore, the proposed scheme represents a particularly effective technique for the solution of large-scale UC problems.

We address the problem of statistical machine translation from highly inflective language to less inflective one. The characteristics of inflective languages are generally not taken into account by the statistical machine translation system. Existing translation systems often treat different inflected word forms of the same lemma as if they were independent of each other, although some interdependencies exist. On the other hand we know that if we reduce inflected word forms to common lemmas, some information is lost. It would be reasonable to eliminate only the variations in inflected word forms, which are not relevant for translation. Inflectional features of words are defined by morpho-syntactic descriptions (MSD) tags and we want reduce them. To do this the explicit knowledge about both languages (source and target language) is needed. The idea of the paper is to find the information-bearing MSDs in source language by data-driven approach. The task is performed by a global optimization algorithm, named Differential Evolution. The experiments were performed using freely available parallel English–Slovenian corpus SVEZ-IJS, which is lemmatized and annotated with MSD tags. The results show a promising direction toward optimal subset of morpho-syntactic features.

We consider finite population slotted ALOHA where each of n terminals has its own transmission probability pi. Given the overall traffic load λ, the probabilities pi are determined in such a way as to maximize throughput. This is achieved by solving a constrained optimization problem. The results of Abramson (1970) are obtained as a special case. Our recent results are improved (Mathar and Žilinskas, 1993).