Journal:Informatica
Volume 27, Issue 2 (2016), pp. 323–334
Abstract
This paper reviews the interplay between global optimization and probability models, concentrating on a class of deterministic optimization algorithms that are motivated by probability models for the objective function. Some complexity results are described for the univariate and multivariate cases.
Journal:Informatica
Volume 25, Issue 4 (2014), pp. 581–616
Abstract
Abstract
The paper summarizes the results of research on the modeling and implementation of advanced planning and scheduling (APS) systems done in recent twenty years. It discusses the concept of APS system – how it is thought of today – and highlights the modeling and implementation challenges with which the developers of such systems should cope. Some from these challenges were identified as a result of the study of scientific literature, others – through an in-depth analysis of the experience gained during the development of real-world APS system – a Production Efficiency Navigator (PEN system). The paper contributes to APS systems theory by proposing the concept of an ensemble of collaborating algorithms.
Journal:Informatica
Volume 22, Issue 4 (2011), pp. 489–505
Abstract
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.
Journal:Informatica
Volume 22, Issue 4 (2011), pp. 471–488
Abstract
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.
Journal:Informatica
Volume 11, Issue 4 (2000), pp. 469–478
Abstract
The result of simulation of an idealized thin wet film connecting fixed points in the Euclidean plane is a length-minimizing curve. Gradually increasing the exterior pressure we are able to achieve the film configuration near to the Steiner minimal tree. This film evolution may be an interesting tool for solving the Euclidean Steiner problem, but several dead-point situations may occur for a certain location of fixed points. A continuous evolution of the film is impossible by increasing the pressure in these situations. The investigation of dead-point situations gives the ways of overcoming the difficulties of dead-point situations and continuing the film evolution by temporarily decreasing pressure.