I’m preparing for my first global travel for global health, but the net is paying attention to a paper that I think I’ll like, and I want to mention it briefly before I fly.
Computational Complexity and Information Asymmetry in Financial Products is 27 pages of serious TCS, but it is so obviously applicable that people outside of our particular ivory tower, and even outside of academia entirely are blogging and twittering about it, and even reading it!
Freedom to Tinker has a nice summary of this paper, if you want to know what it’s about in a hurry.
Mike Trick makes the salient observation that NP-hard doesn’t mean computers can’t do it. But the assumption that this paper is based on is not about worst-case complexity; it is, as it should be, based on an assumption about the average-case complexity of a particular optimization problem over a particular distribution.
As it turns out, this is an average-case combinatorial optimization problem that I know and love, the densest subgraph problem. My plan is to repeat the problem here, and share some Python code for generating instances of it. Then, you, me, and everyone, can have a handy instance to try optimizing. I think that this problem is pretty hard, on average, but there is a lot more chance of making progress on an algorithm for it than for cracking the P versus NP nut. Continue reading
The Chronicle of Higher Ed has a short piece on public-service applications of computer science that are coming out of a class called Computing for Good (C4G) that TCS star Santosh Vempala co-taught at Georgia Tech last spring.
This is an idea that is emerging in several ACO-related disciplines. Manuela Veloso has been running a similar program at CMU called V-Unit, Karen Smilowitz and Michael Johnson held a session at INFORMS 2007 on community-based operations research, and in 2006 student statisticians started a network of volunteer consultancies called Statistics in the Community.
It’s great to see a tradition of “pro bono” work developing in theoretical fields. It’s not just a way for lawyers to assuage their consciences anymore.
This is the final item in my series on Matching Algorithms and Reproductive Health, and it brings the story full circle, returning to the algorithms side of the show. Today I’ll demonstrate how to actually find minimum-weight perfect matchings in Python, and toss in a little story about . Continue reading
I didn’t make any best-of-the-year lists, but I support the idea. I also support new year’s resolutions, but I’m not going to write about mine.
But the internet picks up the slack.
FlowingData has a 5 Best Data Vis of the year list, which I’m fond of. It includes the beautiful Streamgraphs of Byron and Wattenberg. Their technical report has some fun applications of combinatorial optimization to aesthetics.
Lance Fortnow has a nice Complexity Year in Review on the Computational Complexity Blog. Unfortunately, I don’t have a beautiful illustration of Prasad’s result that Unique Games Conjecture implies semidefinite relaxations have optimal approximation ratios.