What did the new PvsNP proof prove?

I normally don't like to blog about mathematical theory.  I usually leave that for the smart people and theorists.  Yet there is an interesting article out this week from the Science section of the New York Times about the new PvsNP proof from Vinay Deolalikar.  The article is not your typical topical subject matter about what the mathematicians are working on next.  The article instead is about the explosion of activity and dialogue on the internet and around the world about this proof in the mathematical community.  The author is suggesting that the likes of which have not been seen before with these types of theoretical discussions.  I would have to agree with the author.  I also find it very ironic that the Old Grey Lady is reporting on this as well.  Since really the only thing that can really be proven, as this article suggests, is that the old media is nearly dead and the new media has supplanted it.

This brings another interesting thought to how problems are solved now.  The mathematical community is closer now than it ever has been.  This is the age of online crowd sourcing.  If I have an question about Operations Research I go to OR-Exchange.  If I am looking for a professional network contact I go to LinkedIn or INFORMS.  If I need to read about the interests of the Operations Research communities I will go to their blogs.  The convergence of ideas, thoughts, and knowledge is closer now and is only going to get even closer. 

This article is one of the reasons why I am such an advocate of open source software with organizations like GNU and COIN-OR. Open source brings the best of bringing thoughts and ideas together to create a quality product.  Sure there are licensing issues.  This article is a good metaphor in that software licensing is like the "old media".  Licensing is trying to catch up with the new technology but there are still a lot of kinks to work out.  There are even suggestions now that software patents should even eliminated.  I'm not sure what will happen but I do know that open source software is driving a lot of innovation in a much shorter time frame.

So yes I find it ironic that the New York Times is reporting on this proof as if it is new news.  Maybe I'm just too close to the subject so I understand it a little better than the rest of the New York Times readers.  Yet if you are anywhere near the mathematical world you would have already seen the proof and had your own conjectures.   Even if that is the case we can prove now that information and knowledge is faster and easier to obtain than ever.

IEOR Tools Tutorial: Learning XML with R

I have been using a lot of R lately in my work.  R (main site) is an open source statistical computing platform.  Saying R is only used for statistics does not do it justice.  I am finding it to be a really powerful statistical and optimization computing platform.  There seems to be no task that can not be accomplished.  Lately I've been curious about measuring performance with my blog and how it compares to other blogs.  So I thought I would use this opportunity to show how I performed this in R.  I want to rank Operations Research blogs using the Alexa ranking system.  Unfortunately Alexa does not have a search function for Operations Research blogs so I am going to have to get the information myself using R.

This R tutorial is going to be using the package XML.  Packages are used in R to perform specific computational needs that the base R platform can not accomplish on its own.  There are several different packages that can be loaded into R to perform a wide variety of problem instances. 

Favorite Operations Research books from OR-Exchange

A while ago I posted a question on OR-Exchange about some of the favorite Operations Research books that OR-Exchange members like to reference.  I was rather pleased with the response.  Of course there are great books on the subject of Operations Research.  The best part of OR-Exchange allows for the community to vote up the favorites.  A lot of these books are just plain good to have in your desk drawer or in your work study.  I have to admit that I have not read all of these books.  So this gives me a good excuse to go get them and perhaps offer up some reviews in the future.

So in order of OR-Exchange votes here are the favorite Operations Research books.

1.  Applied Mathematical Programming by Bradley, Hax, Magnanti.

Applied Mathematical Programming

Also available at http://web.mit.edu/15.053/www/ but if you like it you might want to give it a purchase.


2.  Network Flows:  Theory, Algorithms, and Applications by Ahuja, Magnanti, Orlin
















3.  Linear Programming by Chvatal















4.  Model Building in Mathematical Programming by Williams

















5.  Introduction to Operations Research by Hillier, Lieberman















6.  50 Years of Integer Programming by Juenger, Liebling, Naddef, Nemhauser, Pulleyblank, Reinelt, Rinaldi, Wolsey















7.  The Traveling Salesman Problem: A Computational Study by Applegate, Bixby, Chvatal, Cook















8.  Tabu Search by Glover, Laguna














 9.  Prisoner's Dilemma by Poundstone















10.  Serious Play by Schrage















11.  The Fifth Discipline by Senge















12.  The Predictioneer's Game by Mesquita















13.  Optimization Algorithms for Networks and Graphs by Evans, Minieka

Kaggle introduces new Chess rating competition

Kaggle, home of the statistics and predictive modeling competitions, is introducing its latest contest Elo Versus The Rest of The World.  The competition is being organized by Jeff Sonas who is a chess-metrics afficionado himself.  Jeff describes his history with rating chess players and why he wanted to start such a competition with Kaggle.

This looks to be a really interesting modeling competition with already more than 40 submissions in the leaderboard.  The interesting note about this competition is that the Elo rating system itself is going to be making an appearance on the leaderboard.  This means that if no one beats the Elo system than there is no declared winner.  Although it looks like someone has beaten Elo at its game already.  Elo will be on the leaderboard as a benchmark to make sure that the competition is proving its worth.

I hope to get a chance to make an appearance on the leaderboard.  I am involved in Kaggle's INFORMS 2010 Data Mining contest.  I'm barely hanging on to the top 10 in that competition.  There are some pretty good models to compete against in that group. 

New Look for IEOR Tools

I think I'm following the trend of a lot of OR bloggers and updating the look of the site.  I'm not sure if it is a more modern Web 2.0 look but at least its a little more refined.  I finally got around to fixing the font on the title.  Anyway let me know if you like it.

R IDE for Linux and Gnome

I have been using R in my work recently.  I have also been using R at home to do some tinkering.  In my work environment I use Windows (none too pleased).  I find using the regular R console with Textpad makes for a good Windows development environment.  I haven't been able to replicate this at my home.  At my home I have Ubuntu as my operating system.  I have been searching for a comparable R environment for my home.

That is until now.  The statistics blog at Stattler.com did some research on this very topic of R and Ubuntu.  They found a plugin for R with the text editor Gedit that works wonders.  The plugin is called Rgedit and is very easy to install.  Stattler offers a simple instruction for installation.  Also Stattler has a great review of the Rgedit plugin. Rgedit is very similar in layout to usual gedit text editor except it splits the panes of the screen for code and R output.

Some of the highlights of the Rgedit plugin include:

• Split screen of panes and can be turned on and off
• Syntax highlighting specific to the R code
• Single line or batch processing of R scripts
• Multiple R workspaces can be run
• Shortcut keys can be created and customized

This plugin suits my needs just fine for my Ubuntu uses with R.  There are many other IDEs for R that you may find suit your needs better.  The the beauty of open source software is that there never seems to be a shortage of options.