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        "author": "Tom Janson",
        "author_email": "tom.janson@rwth-aachen.de",
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            "Development Status :: 3 - Alpha",
            "Intended Audience :: Science/Research",
            "License :: OSI Approved :: MIT License",
            "Programming Language :: Python",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": "Heuristics for derivative-free optimization\n===========================================\n\n    Status: Experimental / alpha \u2013 do not use yet\n\nThis library currently implements\n`particle swarm optimization <https://en.wikipedia.org/wiki/Particle_swarm_optimization>`_\nand offers base classes to quickly implement other (meta-)heuristic\noptimization algorithms for continuous domains (as opposed to discrete /\ncombinatorial optimization).\n\nScope and Audience\n------------------\n\nHeuristic optimization algorithms (sometimes called\n`metaheuristics <https://en.wikipedia.org/wiki/Metaheuristic>`_)\naim to find approximate global optima on problems that are intractable for\nexact algorithms. They make no guarantees regarding the optimality of the\nresult (in particular, they are *not*\n`approximation algorithms <https://en.wikipedia.org/wiki/Approximation_algorithm>`_).\n\nOn the upside, these heuristics make few \u2013 if any \u2013 assumptions about the\n`objective function <https://en.wikipedia.org/wiki/Optimization_problem#Continuous_optimization_problem>`_:\nIt can be non-differentiable or even discontinuous and may have multiple local\nand global minima.\n\n.. In practical applications (e.g., engineering, biology,\n   finance) such \u201chard\u201d objective functions are common.\n   The terms `*black-box* <https://scholar.google.com/scholar?cluster=5023697708382309327>`_\n   or `*derivative-free* <https://scholar.google.com/scholar?cluster=13996631775177561404>`_\n   are used to denote that the analytical propierties or specifically the\n   derivatives are not known.\n   Finally, the objective function may not be a function in the mathematical\n   sense at all: It may return (slightly) different values when repeatedly\n   evaluated for the same argument, e.g., if it is the result of a simulation or\n   an imprecise measurement.\n\nHowever, this library originated from a specific use case and thus makes some\nassumptions (which may also evolve in the future).\nE.g.,\n\n-  we assume that objective function evaluations are \u201ccostly\u201d\n   (measured in seconds rather than milliseconds, so that an algorithm\u2019s\n   implementation itself is certainly not a performance bottleneck),\n-  we only handle \u201csoft\u201d constraints using\n   `penalties <https://en.wikipedia.org/wiki/Penalty_method>`_,\n-  we may take liberties when converting real-valued inputs to floating-point\n   or rational representations (due to numeric properties of our problems).\n\nNow, even if this still sounds like a good fit for your project, at this point\nyou should probably consider using a more mature alternative or indeed rolling\nyour own solution tailored to your precise problem.\n\n.. If you end up using this library, by all means get in touch and let us know\n   what your field of application is \u2013 we\u2019re curious!\n\nInstallation\n------------\n\n::\n\n    pip install heuristic_optimization\n\nUsage\n-----\n\nSee ``examples/``.\n\nCredits\n-------\n\nBoth `tisimst/pyswarm <https://github.com/tisimst/pyswarm>`_ and\n`ljvmiranda921/pyswarms <https://github.com/ljvmiranda921/pyswarms>`_ implement\nparticle swarm optimization in Python and served as inspiration (but did not\nquite fit the use case).\n",
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