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    "info": {
        "author": "Xinzhe Wu",
        "author_email": "xinzhe.wu1990@gmail.com",
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        "description": "SMG2S\n\nIterative linear algebra methods are the important parts of the overall computing\ntime  of  applications  in  various  fields  since  decades.   Recent  research  related\nto social networking, big data, machine learning and artificial intelligence has\nincreased  the  necessity  for  non-hermitian  solvers  associated  with  much  larger\nsparse matrices and graphs.  The analysis of the iterative method behaviors for\nsuch problems is complex, and it is necessary to evaluate their convergence to\nsolve extremely large non-Hermitian eigenvalue and linear problems on parallel\nand/or  distributed  machines.   This  convergence  depends  on  the  properties  of\nspectra.  Then,  it is necessary to generate large matrices with known spectra\nto benchmark the methods.  These matrices should be non-Hermitian and non-\ntrivial, with very high dimension.  A scalable parallel matrix generator SMG2S\nthat uses the user-defined spectrum to construct large-scale sparse matrices and\nensures their eigenvalues as the given ones with high accuracy is implemented\nbased on MPI and C++11.  This is the Python interface of SMG2S.\n\n\n",
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        "license": "GNU Lesser General Public License v3.0",
        "maintainer": "",
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        "name": "smg2s",
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        "summary": "SMG2S: Scalable Matrix Generator with Given Spectrum",
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