I like the idea, it might be helpful to checkout the existing constrained interface: http://www.ensmallen.org/docs.html#constrained-functions as well.
> On 22. Mar 2019, at 03:50, sangyunxin <[email protected]> wrote: > > Hi Marcus, > Thank you very much for your explanation. It was great help. > For this project, I think we can use penalty method > <https://en.wikipedia.org/wiki/Penalty_method> or some improved algorithm > based on this. The essence of this method lies in replacing a constrained > problem by a series of unconstrained problems. By this means, we may come up > with a working optimizer which supports both. > Please let me know what you think. > Thank you for your time and consideration. > Sent from YoMail for Gmail <http://www.yomail.com/?utm_source=signature> > 发件人： Marcus Edel <mailto:[email protected]> > 发送时间： 2019-03-21 06:25:17 > 收件人： sangyunxin <mailto:[email protected]> > 抄送： mlpack <mailto:[email protected]> > 主题： Re: [mlpack] Particle swarm optimization for GSOC-19 > Hello Sang, > > welcome, you are right ideally we can come up with an implementation that > supports both, but if we can't e.g. if we are going to implement a PSO variant > that only works on one there is no need to merge both approaches. > > I hope anyything I said makes sense, let me know if I should clarify anything. > > Thanks, > Marcus > >> On 19. Mar 2019, at 03:07, sangyunxin <[email protected] >> <mailto:[email protected]>> wrote: >> >> Dear all, >> My name is Sang Yunxin, and I am a senior student from Whuhan University, >> China. I want to participate in GSoC 2019 and take part in establishing >> "Particle swarm optimization". >> According to my understanding of this idea and previous discussions about >> this project, I will work to provide a PSO optimizer which works for both >> unconstrained and constrained problems to complement mlpack optimizer API —— >> ensmallen. Do I have a correct understanding for this project? >> Thank you, and I look forward to hearing from you. >> _______________________________________________ >> mlpack mailing list >> [email protected] <mailto:[email protected]> >> http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack >> <http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack> >
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