Andreas Stathopoulos - Academia.edu (original) (raw)

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Papers by Andreas Stathopoulos

SIAM Journal on Scientific Computing, 1998

ITSC 2001. 2001 IEEE Intelligent Transportation Systems. Proceedings (Cat. No.01TH8585), 2001

Traffic flow modeling and forecasting has attracted much interest in current literature because o... more Traffic flow modeling and forecasting has attracted much interest in current literature because of its importance in both the theoretical and empirical aspects of ITS deployment and congestion. Despite the importance of modeling traffic flows, most of the literature has concentrated on univariate modeling of flow time-series with freeway data. The approaches considered are the univariate spectral analysis and cross-spectral

Transportation Research Record, 2001

Performance Evaluation, 2005

Journal of Computational and Applied Mathematics, 1995

atlas-conferences.com

... for eigenvalue calculations NS Stylianopoulos Optimal Complex Semi-iterative Methods Applied ... more ... for eigenvalue calculations NS Stylianopoulos Optimal Complex Semi-iterative Methods Applied to SOR in the Case of Intersecting Lines Spectra Daniel B Szyld Algebraic Theory of Schwarz Methods for Domain Decomposition Panagiota Tsompanopoulou Analysis of an ...

IEEE International Symposium on High Performance Distributed Computing, 2002

Clusters of workstations have become a cost-effective means of performing scientific computations... more Clusters of workstations have become a cost-effective means of performing scientific computations. However, large network latencies, resource sharing, and heterogeneity found in networks of clusters and Grids can impede the performance of applications not specifically tailored for use in such environments. A typical example is the traditional fine grain implementations of Krylov-like iterative methods, a central component in many scientific

The focus of this paper is on numerical methods for finding a few eigenvalues andeigenvectors of ... more The focus of this paper is on numerical methods for finding a few eigenvalues andeigenvectors of a large sparse matrix. New preconditioning schemes are proposed forimproving the effectiveness of a few methods for computing eigenvalues and eigenvectors.The basic framework of the preconditioned eigenvalue methods we consider isthat of the Arnoldi method and the related Davidson method. Within this framework,it is

We present a new algorithm that computes eigenvalues and eigenvectors of a Hermitian positive def... more We present a new algorithm that computes eigenvalues and eigenvectors of a Hermitian positive definite matrix while solving a linear system of equations with Conjugate Gradient (CG). Traditionally, all the CG iteration vectors could be saved and recombined through the eigenvectors of the tridiagonal projection matrix, which is equivalent theoretically to unrestarted Lanczos. Our algorithm capitalizes on the iteration vectors

Siam Journal on Scientific Computing, 1996

The Davidson method is a popular preconditioned variant of the Arnoldi method for solvinglarge ei... more The Davidson method is a popular preconditioned variant of the Arnoldi method for solvinglarge eigenvalue problems. For theoretical, as well as practical reasons the two methodsare often used with restarting. Frequently, information is saved through approximated eigenvectorsto compensate for the convergence impairment caused by restarting. We call thisscheme of retaining more eigenvectors than needed `thick restarting", and prove that thickrestarted,

Iterative methods for solving large, sparse, symmetric eigenvalue problems oftenencounter converg... more Iterative methods for solving large, sparse, symmetric eigenvalue problems oftenencounter convergence difficulties because of ill-conditioning. The GeneralizedDavidson method is a well known technique which uses eigenvalue preconditioningto surmount these difficulties. Preconditioning the eigenvalue problem entails moresubtleties than for linear systems. In addition, the use of an accurate conventionalpreconditioner (i.e., as used in linear systems) may cause deterioration of convergence...

International Parallel and Distributed Processing Symposium/International Parallel Processing Symposium, 2004

Dismal performance often results when the memory re- quirements of a process exceed the physical ... more Dismal performance often results when the memory re- quirements of a process exceed the physical memory avail- able to it. Moreover, significant throughput reduction is ex- perienced when this process is part of a synchronous par- allel job on a non-dedicated computational cluster. A pos- sible solution is to develop programs that can dynamically adapt their memory usage according to

International Conference on Supercomputing, 2001

Clusters of workstations (COWs) and SMPs have become popular and cost effective means of solving ... more Clusters of workstations (COWs) and SMPs have become popular and cost effective means of solving scientific problems. Because such environments may be heterogenous and/or time shared, dynamic load balancing is central to achieving high performance. Our thesis is that new levels of sophistication are required in parallel algorithm design and in the interaction of the algorithms with the runtime system.

This paper describes the PRIMME software package for the solving large, sparse Hermitian and real... more This paper describes the PRIMME software package for the solving large, sparse Hermitian and real symmetric eigenvalue problems. The difficulty and importan ce of these problems have increased over the years, necessitating the use of preconditioning and near optimally converging iterative methods. On the other hand, the complexity of tuning or even using such methods has kept them outside the