Computational Electromagnetics Research Center (BİLCEM)

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Browsing Computational Electromagnetics Research Center (BİLCEM) by Subject "Approximation algorithms"

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    Algebraic acceleration and regularization of the source reconstruction method with the recompressed adaptive cross approximation
    (IEEE, 2014) Kazempour, Mahdi; Gürel, Levent
    We present a compression algorithm to accelerate the solution of source reconstruction problems that are formulated with integral equations and defined on arbitrary three-dimensional surfaces. This compression technique benefits from the adaptive cross approximation (ACA) algorithm in the first step. A further error-controllable recompression is applied after the ACA. The numerical results illustrate the efficiency and accuracy of the proposed method. © 2014 IEEE.
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    Effective preconditioners for large integral-equation problems
    (IET, 2007-11) Malas, Tahir; Ergül, Özgür; Gürel, Levent
    We consider effective preconditioning schemes for the iterative solution of integral-equation methods. For parallel implementations, the sparse approximate inverse or the iterative solution of the near-field system enables fast convergence up to certain problem sizes. However, for very large problems, the near-field matrix itself becomes too crude approximation to the dense system matrix and preconditioners generated from the near-field interactions cannot be effective. Therefore, we propose an approximation strategy to the multilevel fast multipole algorithm (MLFMA) to be used as a preconditioner. Our numerical experiments reveal that this scheme significantly outperforms other preconditioners. With the combined effort of effective preconditioners and an efficiently parallelized MLFMA, we are able to solve targets with tens of millions of unknowns in a few hours.
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    Parallel preconditioners for solutions of dense linear systems with tens of millions of unknowns
    (2007-11) Malas, Tahir; Ergül, Özgür; Gürel, Levent
    We propose novel parallel preconditioning schemes for the iterative solution of integral equation methods. In particular, we try to improve convergence rate of the ill-conditioned linear systems formulated by the electric-field integral equation, which is the only integral-equation formulation for targets having open surfaces. For moderate-size problems, iterative solution of the near-field system enables much faster convergence compared to the widely used sparse approximate inverse preconditioner. For larger systems, we propose an approximation strategy to the multilevel fast multipole algorithm (MLFMA) to be used as a preconditioner. Our numerical experiments reveal that this scheme significantly outperforms other preconditioners. With the combined effort of effective preconditioners and an efficiently parallelized MLFMA, we are able to solve targets with tens of millions of unknowns, which are the largest problems ever reported in computational electromagnetics. ©2007 IEEE.
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    The solution of large EFIE problems via preconditioned multilevel fast multipole algorithm
    (Institution of Engineering and Technology, 2007) Malas, Tahir; Gürel, Levent
    We propose an effective preconditioning scheme for the iterative solution of the systems formulated by the electric- field integral equation (EFIE). EFIE is notorious for producing difficult-to-solve systems. Especially, if the target is complex and the utilized frequency is high, it becomes a challenge to solve these dense systems with even robust solvers such as full GMRES. For this purpose, we use an inner-outer solver scheme and use an approximate multilevel fast multipole algorithm for the inner solver to provide a very efficient approximation to the dense linear system matrix. We explore approximation level and inner-solver accuracy to optimize the efficiency of the inner-outer solution scheme. We report the solution of large EFIE systems of several targets to show the effectiveness of the proposed approach.