Computer languages and Applied Math

September 8, 2010
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(This article was first published on Maximize Productivity with Industrial Engineer and Operations Research Tools, and kindly contributed to R-bloggers)

There is no question that computer languages have helped pushed the envelope for applied mathematics.  It is hard to imagine where we would be without airline scheduling, supply chain management, or inventory control if it were not for all of the great advances in optimization and statistical computing.  I have thought a lot about the convergence of computing and Operations Research.  In fact I brought up a discussion on the topic on OR-Exchange with the question "Is programming skills a requirement for today's OR practitioner?"  You would think with all of the advances in computing that programming would be simpler but that is not the case.

There is an interesting debate in the R-project community about the shortcomings of the R language.  Xi'an Og posted a discussion on R shortcomings re-posted from another blog.  The consensus of the R community seems to be that R is an inferior language but has a brilliant library of resources.  So where does that leave the practioner?  Does the practioner need to update their coding skills and develop something better in another computer language?  I find it really interesting that some of the first solutions to this debate is to scrap everything and start over.

I don't think this debate is ever going to change.  The computer is always going to be a valuable tool for the Operations Research practitioner.  The tools we use to complete our daily tasks need to ubiquitous but also readily available.  Let's just say that the slide rule is not going to be making any sort of comeback. 

I believe that the Open Source model has a real advantage here over the proprietary counterparts in this debate.  The community has a lot of input into Open Source software.  It is often called a meritocracy.  The best solutions continue while those that do not go away in obscurity in the Open Source model.  This is one of the reasons why I advocate Open Source software.  In the end I think R is going to be fine.  There will be advances, possible even forks of the software, but there will always be progress.  The only limitations seem to be of what we could dream.

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