Finite-size, magnetic and chemical-potential effects on first-order phase transitions

We perform a study about effects of an applied magnetic field and a finite chemical potential on the size-dependent phase structure of a first-order transition. These effects are introduced by using methods of quantum fields defined on toroidal spaces, and we study in particular the case of two compactified dimensions, imaginary time and a spatial one (a heated film). It is found that for any value of the applied field, there is a minimal size of the system, independent of the chemical potential, below which the transition disappears.Comment: 19 pages, 3 figures, version accepted for publication in Phys. Lett.

Partially ordered distributed computations on asynchronous point-to-point networks

Asynchronous executions of a distributed algorithm differ from each other due to the nondeterminism in the order in which the messages exchanged are handled. In many situations of interest, the asynchronous executions induced by restricting nondeterminism are more efficient, in an application-specific sense, than the others. In this work, we define partially ordered executions of a distributed algorithm as the executions satisfying some restricted orders of their actions in two different frameworks, those of the so-called event- and pulse-driven computations. The aim of these restrictions is to characterize asynchronous executions that are likely to be more efficient for some important classes of applications. Also, an asynchronous algorithm that ensures the occurrence of partially ordered executions is given for each case. Two of the applications that we believe may benefit from the restricted nondeterminism are backtrack search, in the event-driven case, and iterative algorithms for systems of linear equations, in the pulse-driven case

On the efficiency of a genetic algorithm for the multiprocessor scheduling problem

In the multiprocessor scheduling problem a given program is to be scheduled in a given multiprocessor system such that the program's execution time is minimized. This problem being very hard to solve exactly, many heuristic methods for finding a suboptimal schedule exist. An efficient genetic algorithm which introduces some knowledge about the scheduling problem represented by the use of a list heuristic in the crossover and mutation genetic operations was recently proposed [3] in this paper we investigate the efficiency of this genetic algorithm from a theoretical point of view. In particular , we demonstrate the ability of the knowledge-augmented crossover operator to generate all the space of feasible solutions