Discrete optimization @ Oaxaca - I

I'm at the BIRS-CMO Workshop on Discrete Optimization in beautiful Oaxaca (in Mexico). Unlike other typical workshops, the organizers have tried hard to encourage lots of discussion and interactions among the participants - big props to Jesus De Loera and Jon Lee! I'm hoping to write snippets on talks/discussion that I found particularly interesting (yes, it'll be a biased view :-).

The meeting started with an apt talk by Dan Bienstock on LP formulations for polynomial optimization problems (based on this paper; Dan has also posted the slides). He started with a motivating problem - the optimal power flow (OPF) problem, which motivates the use of the treewidth of the intersection graph of the constraints as the parameter which controls the complexity of the proposed reformulation operator. And real-life power grids often have small treewidths. The reformulation operator produces linear programming approximations that attain provable bounds for mixed integer polynomial problems where the variables are either binary, or require \(0 \leq x_j \leq 1\).

Informally, the intersection graph of a system of constraints has one vertex for each variable \(x_j\), and edge \((x_i,x_j)\) is present when both \(x_i\) and \(x_j\) appear in some constraint. The simple example of a subset sum (or knapsack) problem was insightful. With the single constraint being \(x_1+\dots+x_n \leq \beta\), a constraint graph could have \(n+1\) vertices \({0,1,\dots,n}\), with the \(n\) edges \((0,j)\) for each \(j\) corresponding to \(x_j\) (node \(0\) is a "dummy" node here). The treewidth of this "star" graph is \(1\). The other key trick employed is the use of binary variables to approximate a continuous variable \(0 \leq x \leq 1\) (attributed originally to Glover). For a given error term \(0 < \gamma < 1\), we can approximate \(x \approx \sum_{j=1}^L \left(1/2^h\right) y_h\), where \(y_h\) are binary variables. With \(L = \lceil \log_2 (1/\gamma) \rceil \), we can get \(x \leq \sum_{j=1}^L \left(1/2^h\right) y_h \leq x + \gamma\). This step helps to get pure binary problems in place of the mixed integer problems. The treewidth gets blown up by \(L\), but things still work out nicely. This paper seems to have lots of nice and deep "tricks" (I hope to study it in detail).

There were several other interesting talks, and a very interactive problem session to conclude the day. Oh, and I learned a new terminology used in the power industry from Shabbir Ahmed's talk: a prosumer is someone who both produces and consumes power. I'm wondering why that is more apt than a conducer...

There was some lively discussion over dinner about how the optimization and operations research communities have failed to sell itself as well as the CS community (as a whole, or even the CS theory community by itself). Large membership sizes of ACM vs INFORMS and similarly large NSF budgets for CS vs OR were cited as indicators. There was also an anecdote mentioned about how back in the 1980s when NSF funding for algorithms/CS theory was on the decline, a group of several top big names from that field submitted a memo/petition to the lawmakers in DC, and also convinced them in person that "algorithms/theory is as fundamental as cosmology" (needless to say, I'm paraphrasing to a huge extent here!). And yes, they managed to restore the funding flow. The optimization community tried a bit of the same trick with Karmarkar's interior point algorithm for LP. May be we optimizers should try harder - not just from the point of view of securing funding, but also from the point of view of our students getting better industry jobs!