A parallel iterative nonoverlapping domain decomposition method is proposed and analyzed for elliptic problems. Each iteration in this method contains two steps. In the rst step, at the interface of two subdomains, one subdomain problem requires that Dirichlet data be passed to it from the previous iteration level, while the other subdomain problem requires Neumann data be passed to it. In the ...

Incomplete LU factorization (ILU) techniques are a well-known class of preconditioners, often used in conjunction with Krylov accelerators for the iterative solution of linear systems of equations. However, for certain problems, ILU factorizations can yield factors that are unstable, and in some cases quite dense. Reordering techniques based on permuting the matrix prior to performing the facto...

Parallel sparse direct solvers are now able to solve efficiently real-life three-dimensional problems having in the order of several millions of equations. They are, however, constrained by prohibitive memory requirements. Iterative methods on the other hand require much less memory, but they often fail to solve ill-conditioned systems. We propose an hybrid direct-iterative method which aims at...

Caching of documents is an important concern on the Web. It is a major win in all situations where bandwidth is limited. Unfortunately, the increasing spread of dynamically generated documents seriously hampers traditional caching techniques in browsers and on proxy servers. WASH/CGI is a Haskell-based domain specific language for creating interactive Web applications. The Web pages generated b...

This paper focuses on the application of a recently proposed non-iterative inversion method for three different imaging techniques for two phases materials. Specifically, two techniques concern the retrieval of the resistivity of a conductor (electrical resistance tomography and magnetic induction tomography) and one technique concerns the retrieval of the permittivity of a dielectric material ...

Subspace detection and processing is receivingmore attention nowadays as a method to speed up searchand reduce processing overload. Subspace Learningalgorithms try to detect low dimensional subspaces in thedata which minimize the intra-class separation whilemaximizing the inter-class separation. In this paper wepresent a novel technique using the maximum significance...