Browsing by Subject "Linear Equations"

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Open Access
Comparison of partitioning techniques for two-level iterative solvers on large, sparse Markov chains
(SIAM, 2000) Dayar T.; Stewart, W. J.
Experimental results for large, sparse Markov chains, especially the ill-conditioned nearly completely decomposable (NCD) ones, are few. We believe there is need for further research in this area, specifically to aid in the understanding of the effects of the degree of coupling of NCD Markov chains and their nonzero structure on the convergence characteristics and space requirements of iterative solvers. The work of several researchers has raised the following questions that led to research in a related direction: How must one go about partitioning the global coefficient matrix into blocks when the system is NCD and a two-level iterative solver (such as block SOR) is to be employed? Are block partitionings dictated by the NCD form of the stochastic one-step transition probability matrix necessarily superior to others? Is it worth investigating alternative partitionings? Better yet, for a fixed labeling and partitioning of the states, how does the performance of block SOR (or even that of point SOR) compare to the performance of the iterative aggregation-disaggregation (IAD) algorithm? Finally, is there any merit in using two-level iterative solvers when preconditioned Krylov subspace methods are available? We seek answers to these questions on a test suite of 13 Markov chains arising in 7 applications.
Open Access
A parallel scaled conjugate-gradient algorithm for the solution phase of gathering radiosity on hypercubes
(Springer, 1997) Kurç, T. M.; Aykanat, Cevdet; Özgüç, B.
Gathering radiosity is a popular method for investigating lighting effects in a closed environment. In lighting simulations, with fixed locations of objects and light sources, the intensity and color and/or reflectivity vary. After the form-factor values are computed, the linear system of equations is solved repeatedly to visualize these changes. The scaled conjugate-gradient method is a powerful technique for solving large sparse linear systems of equations with symmetric positive definite matrices. We investigate this method for the solution phase. The nonsymmetric form-factor matrix is transformed into a symmetric matrix. We propose an efficient data redistribution scheme to achieve almost perfect load balance. We also present several parallel algorithms for form-factor computation.