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  • An Adaptive Solver for Systems of Linear Equations

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    Sanderson457581-Accepted.pdf (286.7Kb)
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    Accepted Manuscript (AM)
    Author(s)
    Sanderson, Conrad
    Curtin, Ryan
    Griffith University Author(s)
    Sanderson, Conrad
    Year published
    2020
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    Abstract
    Computational implementations for solving systems of linear equations often rely on a one-size-fits-all approach based on LU decomposition of dense matrices stored in column-major format. Such solvers are typically implemented with the aid of the xGESV set of functions available in the low-level LAPACK software, with the aim of reducing development time by taking advantage of well-tested routines. However, this straight-forward approach does not take into account various matrix properties which can be exploited to reduce the computational effort and/or to increase numerical stability. Furthermore, direct use of LAPACK functions ...
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    Computational implementations for solving systems of linear equations often rely on a one-size-fits-all approach based on LU decomposition of dense matrices stored in column-major format. Such solvers are typically implemented with the aid of the xGESV set of functions available in the low-level LAPACK software, with the aim of reducing development time by taking advantage of well-tested routines. However, this straight-forward approach does not take into account various matrix properties which can be exploited to reduce the computational effort and/or to increase numerical stability. Furthermore, direct use of LAPACK functions can be error-prone for non-expert users and results in source code that has little resemblance to originating mathematical expressions. We describe an adaptive solver that we have implemented inside recent versions of the high-level Armadillo C++ library for linear algebra. The solver automatically detects several common properties of a given system (banded, triangular, symmetric positive definite), followed by solving the system via mapping to a set of suitable LAPACK functions best matched to each property. The solver also detects poorly conditioned systems and automatically seeks a solution via singular value decomposition as a fallback. We show that the adaptive solver leads to notable speedups, while also freeing the user from using direct calls to cumbersome LAPACK functions.
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    Conference Title
    2020 14th International Conference on Signal Processing and Communication Systems (ICSPCS)
    DOI
    https://doi.org/10.1109/icspcs50536.2020.9309998
    Copyright Statement
    © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Publication URI
    http://hdl.handle.net/10072/400948
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    • Conference outputs

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