Carlos Balsa

The long-run relationship between the construction sector and the national economy in Cape Verde

The relationship between a countrys level of activity in the construction industry and its stage of economic development is a complex one. Several studies over the last forty years, based mainly on cross sectional data, found a positive association between national income and several measures of the construction industry activity. early studies were concerned with the role of the construction sector, as part of physical capital, in the promotion of economic growth and development. a dominant paradigm that later emerged is the ‘Bon curve’ or the inverted U-shaped pattern of development. More recent research, based on longitudinal analysis, has also pointed to the non-linear relationship between the share of construction in GDP and the level of income per capita. Using time-series data drawn from the United nations, this study applies an econometric methodology to assess the validity of the underlying propositions in a low-middle income economy-Cape Verde—over the long period of 38 years. The findings are in line with the assumptions that in the upward growth trend in developing countries, the pattern of the construction industry tends to follow that of the general economy.

Monitoring the block conjugate gradient convergence within the inexact inverse subspace iteration

We propose an algorithm called BlockCGSI to compute some partial spectral information related to the ill-conditioned part of a given coefficient matrix. This information can then be used to improve the solution of consecutive linear systems with the same coefficient matrix and changing right-hand sides. The BlockCGSI algorithm combines the block Conjugate Gradient with the inverse Subspace Iteration. We indicate how to reduce the total amount of computational work by controlling appropriately the accuracy when solving the linear systems at each inverse iteration. We also improve the global convergence of the algorithm by means of polynomial filters.

Partial spectral information from linear systems to speed-up numerical simulations in computational fluid dynamics

It was observed that all the different linear systems arising in an iterative fluid flow simulation algorithm have approximately constant invariant subspaces associated with their smallest eigenvalues. For this reason, we propose to perform one single computation of the eigenspace associated with the smallest eigenvalues, at the beginning of the iterative process, to improve the convergence of the Krylov method used in subsequent iterations of the fluid flow algorithm by means of this pre-computed partial spectral information. The Subspace Inverse Iteration Method with Stabilized Block Conjugate Gradient is our choice for computing the spectral information, which is then used to remove the effect of the smallest eigenvalues in two different ways: either building a spectral preconditioner that shifts these eigenvalues from almost zero close to the unit value, or performing a deflation of the initial residual in order to remove parts of the solution corresponding to the smallest eigenvalues. Under certain conditions, both techniques yield a reduction of the number of iterations in each subsequent runs of the Conjugate Gradient algorithm.

Clustering entrepreneurship aspirations: innovation, growth and international orientation of activities

It is sometimes argued that nations differ in their underlying entrepreneurial spirit. So, in recent years, more researchers started being interested in the analysis of entrepreneurship across regions. Associated with entrepreneurship comes the concept of innovation. Innovation allows the creation of new firms and/or ensures the survival of the existing ones, and in both cases, generates growth. Applying the statistical technique of cluster analysis to a country dataset gathered by the Global Entrepreneurship Monitor (GEM) this paper groups countries into four clusters regarding their entrepreneurial activity and the aspirations of national entrepreneurs concerning innovation, business growth and international orientation of their activities. This is particularly important not only because the achievement of a relevant national entrepreneurship rate depends on the social and economic business environment but also because it is not known if there are particular regions where the entrepreneurial activity is characterised by special patterns of entrepreneurship aspirations.

An hybrid approach for the parallelization of a block iterative algorithm

The Cimmino method is a row projection method in which the original linear system is divided into subsystems. At every iteration, it computes one projection per subsystem and uses these projections to construct an approximation to the solution of the linear system. The usual parallelization strategy in block algorithms is to distribute the different blocks on the available processors. In this paper, we follow another approach where we do not perform explicitly this block distribution to processors within the code, but let the multi-frontal sparse solver MUMPS handle the data distribution and parallelism. The data coming from the subsystems defined by the block partition in the Block Cimmino method are gathered in an unique block diagonal sparse matrix which is analysed, distributed and factorized in parallel by MUMPS. Our target is to define a methodology for parallelism based only on the functionalities provided by general sparse solver libraries and how efficient this way of doing can be.

Improving the numerical simulation of an airflow problem with the BlockCGSI algorithm

Partial spectral information associated with the smallest eigenvalues can be used to improve the solution of successive linear systems of equations, namely in the simulation of time-dependent partial differential equations, where at each time step there are several systems with the same spectral properties to be solved. We propose to perform a partial spectral decomposition with the BlockCGSI algorithm in the first time step, and exploit this information to improve the convergence of the Conjugate Gradient algorithm in the solution of the following linear systems. We describe in summary the BlockCGSI algorithm, that is a combination of the block Conjugate Gradient (blockCG) with the Inverse Subspace Iteration. Then, we validate the accelerating strategy in the simulation of the flow around an airplane wing, where the Conjugate Gradient is accelerated through the deflation of the starting residual.

Optimization Clustering Techniques on Register Unemployment Data

An important strategy for data classification consists in organising data points in clusters. The k-means is a traditional optimisation method applied to cluster data points. Using a labour market database, aiming the segmentation of this market taking into account the heterogeneity resulting from different unemployment characteristics observed along the Portuguese geographical space, we suggest the application of an alternative method based on the computation of the dominant eigenvalue of a matrix related with the distance among data points. This approach presents results consistent with the results obtained by the k-means.