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        "author": "Nico Schl\u00f6mer",
        "author_email": "nico.schloemer@gmail.com",
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            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 3",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Mathematics",
            "Topic :: Scientific/Engineering :: Physics"
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        "description": "# optipy\n\n[![CircleCI](https://img.shields.io/circleci/project/github/nschloe/optipy/master.svg)](https://circleci.com/gh/nschloe/optipy)\n[![codecov](https://img.shields.io/codecov/c/github/nschloe/optipy.svg)](https://codecov.io/gh/nschloe/optipy)\n[![Codacy grade](https://img.shields.io/codacy/grade/2741eedd98a24ee0b65319c1f436f40e.svg)](https://app.codacy.com/app/nschloe/optipy/dashboard)\n[![PyPi Version](https://img.shields.io/pypi/v/optipy.svg)](https://pypi.org/project/optipy)\n[![GitHub stars](https://img.shields.io/github/stars/nschloe/optipy.svg?logo=github&label=Stars)](https://github.com/nschloe/optipy)\n\noptipy contains a generic optimization/minimization method. Its creation was\nmotivated by the absence of an implementation of Newton's method with a custom\nHessian solver in SciPy (see [this\nbug](https://github.com/scipy/scipy/issues/8756)).\n\n\nThe mandatory Rosenbrock example:\n```python\na = 1.0\nb = 100.0\n\ndef fun(x):\n    return (a-x[0])**2 + b*(x[1] - x[0]**2)**2\n\ndef jac(x):\n    return numpy.array([\n        -2*(a-x[0]) - 4*b*(x[1] - x[0]**2) * x[0],\n        2*b*(x[1] - x[0]**2)\n        ])\n\ndef hess_inv(x, grad):\n    hess = numpy.array([\n        [2 + 8*b*x[0]**2 - 4*b*(x[1] - x[0]**2), -4*b*x[0]],\n        [-4*b*x[0], 2*b]\n        ])\n    return -numpy.linalg.solve(hess, grad)\n\nsol = optipy.minimize(\n    fun=fun,\n    x0=[-1.0, 3.5],\n    jac=jac,\n    get_search_direction=hess_inv,\n    atol=1.0e-5\n    )\n```\nThis is basically the exact Newton method. When setting `get_search_direction`\nto `lambda x, grad: -grad`, one gets the steepest descent method. For larger\ncomputations, one will typically replace this with a tailored solver, e.g., a\npreconditioned Krylov solver.\n\nThe return type is largely compatible with SciPy's generic return type,\n[OptmizeResult](https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html#scipy.optimize.OptimizeResult).\n\n\n### Installation\n\noptipy is [available from the Python Package\nIndex](https://pypi.org/project/optipy/), so simply do\n```\npip install -U optipy\n```\nto install or upgrade. Use `sudo -H` to install as root or the `--user` option\nof `pip` to install in `$HOME`.\n\n\n### Testing\n\nTo run the optipy unit tests, check out this repository and type\n```\npytest\n```\n\n### Distribution\nTo create a new release\n\n1. bump the `__version__` number,\n\n2. publish to PyPi and tag on GitHub:\n    ```\n    $ make publish\n    ```\n\n### License\n\noptipy is published under the [MIT license](https://en.wikipedia.org/wiki/MIT_License).\n\n\n",
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