June 10, 2020 Software Open Access

Tom M. Ragonneau; Zaikun Zhang

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    "description": "<p><a href=\"https://pdfo.net\">PDFO</a> (Powell&#39;s Derivative-Free Optimization solvers) is a cross-platform package providing interfaces for using late Professor&nbsp;<a href=\"https://www.zhangzk.net/powell.html\">M. J. D. Powell</a>&#39;s&nbsp;<a href=\"https://en.wikipedia.org/wiki/Derivative-free_optimization\">derivative-free optimization</a>&nbsp;solvers, including&nbsp;<a href=\"https://en.wikipedia.org/wiki/UOBYQA\">UOBYQA</a>,&nbsp;<a href=\"https://en.wikipedia.org/wiki/NEWUOA\">NEWUOA</a>,&nbsp;<a href=\"https://en.wikipedia.org/wiki/BOBYQA\">BOBYQA</a>,&nbsp;<a href=\"https://en.wikipedia.org/wiki/LINCOA\">LINCOA</a>, and&nbsp;<a href=\"https://en.wikipedia.org/wiki/COBYLA\">COBYLA</a>, which were originally implemented in Fortran 77.</p>\n\n<p>Professor Powell devised these solvers to tackle&nbsp;<a href=\"http://plato.asu.edu/sub/nlores.html#general\">general nonlinear optimization problems</a>&nbsp;of continuous variables with or without constraints using only&nbsp;<a href=\"http://www.damtp.cam.ac.uk/user/na/NA_papers/NA2007_03.pdf\">function values but not derivatives</a>&nbsp;of the objective function or nonlinear constraint functions. In practice, such functions are often black boxes defined by simulations. Consequently, the corresponding optimization problems are often categorized as&nbsp;<a href=\"https://en.wikipedia.org/wiki/Derivative-free_optimization\">black-box optimization</a>&nbsp;or&nbsp;<a href=\"https://en.wikipedia.org/wiki/Simulation-based_optimization\">simulation-based optimization</a>. Problem specified by explicit formulas can probably be handled by other methods more efficiently. See the&nbsp;<a href=\"http://plato.asu.edu/sub/nlores.html#general\">Decision Tree for Optimization Software</a>&nbsp;for more information.</p>\n\n<p>The current version of PDFO supports MATLAB and Python. It relies on&nbsp;<a href=\"https://www.mathworks.com/help/matlab/ref/mex.html\">MEX</a>&nbsp;for MATLAB and&nbsp;<a href=\"https://docs.scipy.org/doc/numpy/f2py/\">F2PY</a>&nbsp;for Python to compile the Fortran solvers and wrap them into user-friendly functions.</p>\n\n<p>Based on Professor Powell&#39;s Fortran code, PDFO is developed by&nbsp;<a href=\"https://www.tom-ragonneau.co/\">Tom M. Ragonneau</a>&nbsp;and&nbsp;<a href=\"https://www.zhangzk.net/\">Zaikun Zhang</a>&nbsp;at the&nbsp;<a href=\"https://www.polyu.edu.hk/ama\">Department of Applied Mathematics</a>,&nbsp;<a href=\"https://www.polyu.edu.hk/\">the Hong Kong Polytechnic University</a>.</p>\n\n<p>See the homepage of PDFO at <a href=\"https://pdfo.net\">https://pdfo.net</a>&nbsp;for more information.&nbsp;</p>", 
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    "title": "PDFO: Cross-Platform Interfaces for Powell's Derivative-Free Optimization Solvers", 
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