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        "description": "# autoptim: automatic differentiation + optimization\n\nDo you have a new machine learning model that you want to optimize, and do not want to bother computing the gradients? Autoptim is for you.\n\n## Short presentation\nAutoptim is a small Python package that blends `autograd` automatic differentiation in `scipy.optimize.minimize`.\n\nThe gradients are computed under the hood using automatic differentiation; the user only provides the objective function:\n\n```python\nimport numpy as np\nfrom autoptim import minimize\n\n\ndef rosenbrock(x):\n    return (1 - x[0]) ** 2 + 100 * (x[1] - x[0] ** 2) ** 2\n\n\nx0 = np.zeros(2)\n\nx_min, _ = minimize(rosenbrock, x0)\nprint(x_min)\n\n>>> [0.99999913 0.99999825]\n```\n\nIt comes with the following features:\n\n- **Natural interfacing with Numpy**: The objective function is written in standard Numpy. The input/ output of `autoptim.minimize` are Numpy arrays.\n\n- **Smart input processing**: `scipy.optimize.minimize` is only meant to deal with one-dimensional arrays as input. In `autoptim`, variables can be multi-dimensional arrays or lists of arrays.\n- **Preconditioning**: Preconditioning is a simple way to accelerate minimization, by doing a change of variables. `autoptim` makes preconditioning straightforward. \n\n\n### Disclaimer\n\nThis package is meant to be as easy to use as possible. As so, some compromises on the speed of minimization are made.\n## Installation\n  To install, use `pip`:\n  ```\n  pip install autoptim\n  ```\n## Dependencies\n- numpy>=1.12\n- scipy>=0.18.0\n- autograd >= 1.2\n\n\n## Examples\nSeveral examples can be found in `autoptim/tutorials`\n\n\n",
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        "license": "MIT",
        "maintainer": "Pierre Ablin",
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