ACCELERATING ITERATIVE ALGORITHMS FOR SYMMETRICAL LINEAR COMPLEMENTARITY-PROBLEMS

We propose a method for accelerating iterative algorithms for solving symmetric linear complementarity problems. The method consists in performing a one-dimensional optimization in the direction generated by a splitting method even for non-descent directions. We give strong convergence proofs and present numerical experiments that justify using this acceleration.

On the dissimilarity between the algorithms for computing the symmetric energy-momentum tensor in ordinary field theory and general relativity

The recipe used to compute the symmetric energy-momentum tensor in the framework of ordinary field theory bears little resemblance to that used in the context of general relativity, if any. We show that if one stal ts fi om the field equations instead of the Lagrangian density, one obtains a unified algorithm for computing the symmetric energy-momentum tensor in the sense that it can be used for both usual field theory and general relativity.

Analysis of heuristic algorithms for the transportation model in static and multistage planning in network expansion systems

The usefulness of the application of heuristic algorithms in the transportation model, first proposed by Garver, is analysed in relation to planning for the expansion of transmission systems. The formulation of the mathematical model and the solution techniques proposed in the specialised literature are analysed in detail. Starting with the constructive heuristic algorithm proposed by Garver, an extension is made to the problem of multistage planning for transmission systems. The quality of the solutions found by heuristic algorithms for the transportation model is analysed, as are applications in problems of planning transmission systems.

Conservation genetics of the endangered Pampas deer (Ozotoceros bezoarticus)

The Pampas deer (Ozotoceros bezoarticus L. 1758) is the most endangered neotropical cervid, and in the past occupied a wide range of open habitats including grassland, pampas, savanna, and cerrado (Brazil) from 5 degrees to 41 degrees S. To better understand the effect of habitat fragmentation on gene flow and genetic variation, and to uncover genetic units for conservation, we examined DNA sequences from the mitochondrial control region of 54 individuals from six localities distributed throughout the present geographical range of the Pampas deer. Our results suggest that the control region of the Pampas deer is one of the most polymorphic of any mammal. This remarkably high variability probably reflects large historic population sizes of millions of individuals in contrast to numbers of fewer than 80 000 today. Gene flow between populations is generally close to one migrant per generation and, with the exception of two populations from Argentina, all populations are significantly differentiated. The degree of gene flow was correlated with geographical distance between populations, a result consistent with limited dispersal being the primary determinant of genetic differentiation between populations. The molecular genetic results provide a mandate for habitat restoration and reintroduction of Pampas deer so that levels of genetic variation can be preserved and historic patterns of abundance can be reconstructed. However, the source of individuals for reintroduction generally should be from populations geographically closest to those now in danger of extinction.

A cost optimization-based design of precast concrete floors using genetic algorithms

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Genetic Algorithms (Binary and Real Codes) for the Optimisation of a Fermentation Process for Butanol Production

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Using traditional heuristic algorithms on an initial genetic algorithm population applied to the transmission expansion planning problem

This paper analyses the impact of choosing good initial populations for genetic algorithms regarding convergence speed and final solution quality. Test problems were taken from complex electricity distribution network expansion planning. Constructive heuristic algorithms were used to generate good initial populations, particularly those used in resolving transmission network expansion planning. The results were compared to those found by a genetic algorithm with random initial populations. The results showed that an efficiently generated initial population led to better solutions being found in less time when applied to low complexity electricity distribution networks and better quality solutions for highly complex networks when compared to a genetic algorithm using random initial populations.