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        "author": "Eloy Romero Alcalde, Andreas Stathopoulos and Lingfei Wu",
        "author_email": "eloy@cs.wm.edu",
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        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Developers",
            "Intended Audience :: Information Technology",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: BSD License",
            "Operating System :: MacOS",
            "Operating System :: Microsoft :: Windows",
            "Operating System :: POSIX",
            "Operating System :: Unix",
            "Programming Language :: C",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Software Development"
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        "description": "PRIMME: PReconditioned Iterative MultiMethod Eigensolver\n========================================================\n\n`primme` is a Python interface to PRIMME_, a high-performance library for computing a few eigenvalues/eigenvectors, and singular values/vectors.\nPRIMME is especially optimized for large, difficult problems.\nReal symmetric and complex Hermitian problems, standard `A x = \\lambda x` and generalized `A x = \\lambda B x`, are supported.\nIt can find largest, smallest, or interior singular/eigenvalues, and can use preconditioning to accelerate convergence.\n\nThe main contributors to PRIMME are James R. McCombs, Eloy Romero Alcalde, Andreas Stathopoulos and Lingfei Wu.\n\nInstall\n-------\n\nYou can install the latest version with `pip`::\n\n    pip install numpy   # if numpy is not installed yet\n    pip install scipy   # if scipy is not installed yet\n    pip install future  # if using python 2\n    conda install mkl-devel # if using Anaconda Python distribution\n    pip install primme\n\nOptionally for building the development version do::\n\n    git clone https://github.com/primme/primme\n    cd primme\n    make python_install\n\nUsage\n-----\n\nThe following examples compute a few eigenvalues and eigenvectors from a real symmetric matrix::\n\n    >>> import Primme, scipy.sparse\n    >>> A = scipy.sparse.spdiags(range(100), [0], 100, 100) # sparse diag. matrix\n    >>> evals, evecs = Primme.eigsh(A, 3, tol=1e-6, which='LA')\n    >>> evals # the three largest eigenvalues of A\n    array([ 99.,  98.,  97.])\n\n    >>> new_evals, new_evecs = Primme.eigsh(A, 3, tol=1e-6, which='LA', ortho=evecs)\n    >>> new_evals # the next three largest eigenvalues\n    array([ 96.,  95.,  94.])\n\n    >>> evals, evecs = primme.eigsh(A, 3, tol=1e-6, which=50.1)\n    >>> evals # the three closest eigenvalues to 50.1\n    array([ 50.,  51.,  49.])\n\n\nThe following examples compute a few eigenvalues and eigenvectors from a generalized Hermitian problem, without factorizing or inverting `B`::\n\n    >>> import Primme, scipy.sparse\n    >>> A = scipy.sparse.spdiags(range(100), [0], 100, 100) # sparse diag. matrix\n    >>> M = scipy.sparse.spdiags(np.asarray(range(99,-1,-1)), [0], 100, 100)\n    >>> evals, evecs = primme.eigsh(A, 3, M=M, tol=1e-6, which='SA')\n    >>> evals\n    array([1.0035e-07, 1.0204e-02, 2.0618e-02])\n\nThe following examples compute a few singular values and vectors::\n\n    >>> import Primme, scipy.sparse\n    >>> A = scipy.sparse.spdiags(range(1, 11), [0], 100, 10) # sparse diag. rect. matrix\n    >>> svecs_left, svals, svecs_right = Primme.svds(A, 3, tol=1e-6, which='SM')\n    >>> svals # the three smallest singular values of A\n    array([ 1.,  2.,  3.])\n\n    >>> A = scipy.sparse.rand(10000, 100, random_state=10)\n    >>> prec = scipy.sparse.spdiags(np.reciprocal(A.multiply(A).sum(axis=0)),\n    ...           [0], 100, 100) # square diag. preconditioner\n    >>> svecs_left, svals, svecs_right = Primme.svds(A, 3, which=6.0, tol=1e-6,\n    ...           precAHA=prec)\n    >>> [\"%.5f\" % x for x in svals.flat] # the three closest singular values of A to 0.5\n    ['5.99871', '5.99057', '6.01065']\n\nFurther examples_.\n\nDocumentation of eigsh_ and svds_.\n\nCiting this code \n----------------\n\nPlease cite (bibtex_):\n\n* A. Stathopoulos and J. R. McCombs *PRIMME: PReconditioned Iterative\n  MultiMethod Eigensolver: Methods and software description*, ACM\n  Transaction on Mathematical Software Vol. 37, No. 2, (2010),\n  21:1-21:30.\n\n* L. Wu, E. Romero and A. Stathopoulos, *PRIMME_SVDS: A High-Performance\n  Preconditioned SVD Solver for Accurate Large-Scale Computations*,\n  J. Sci. Comput., Vol. 39, No. 5, (2017), S248--S271.\n\nLicense Information\n-------------------\n\nPRIMME and this interface is licensed under the 3-clause license BSD.\n\nContact Information \n-------------------\n\nFor reporting bugs or questions about functionality contact `Andreas Stathopoulos`_ by\nemail, `andreas` at `cs.wm.edu`. See further information in\nthe webpage http://www.cs.wm.edu/~andreas/software.\n\n.. _PRIMME: https://github.com/primme/primme\n.. _`Andreas Stathopoulos`: http://www.cs.wm.edu/~andreas/software\n.. _`github`: https://github.com/primme/primme\n.. _`doc`: http://www.cs.wm.edu/~andreas/software/doc/readme.html\n.. _PETSc: http://www.mcs.anl.gov/petsc/\n.. _`bibtex`: https://raw.githubusercontent.com/primme/primme/master/doc/primme.bib\n.. _eigsh: http://www.cs.wm.edu/~andreas/software/doc/pyeigsh.html\n.. _svds: http://www.cs.wm.edu/~andreas/software/doc/pysvds.html\n.. _examples: https://github.com/primme/primme/blob/master/Python/examples.py",
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