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        "description": "\n## objopt\nThis library provides object oriented optimization. This allows...\n\n1. using theoretic values (such as the strong convexity parameter)\n2. object-oriented definitions, both for models and optimization algorithms. This allows...\n    * interacting with the optimization as an object. Want to compute some\n      value partway through? Want to change the values as time goes on?\n    * getting results intermediately (or in the presence of a keyboard\n      interrept)\n    * having callbacks, etc\n\nA typical example:\n\n``` python\ndef get_stats():\n    # ...\n\nmodel = Model()\nopt = SGD(model.loss)\n\ndata = []\nfor _ in range(10):\n    opt.step(steps=10)\n    data += [get_stats(model)]\n```\n\n",
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        "keywords": "optimization",
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        "maintainer": "",
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        "name": "objopt",
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