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Since the inception of optimization as a recognized field of study in mathematics, researchers have been both intrigued and stymied by the difficulty of solving many of the most interesting classes of discrete optimization problems. Even combinatorial problems, though conceptually easy to model as integer programs, have long remained challenging to solve in practice. The last two decades have seen tremendous progress in our ability to solve large-scale discrete optimization problems. These advances have culminated in the approach that we now call branch and cut, a technique (see [19,29,20]) which brings the computational tools of branch and bound algorithms together with the theoretical tools of polyhedral combinatorics. Indeed, in 1998, Applegate, Bixby, Chvátal, and Cook used this technique to solve a Traveling Salesman Problem instance with 13,509 cities, a full order of magnitude larger than what had been possible just a decade earlier [2] and two orders of magnitude larger than the largest problem that had been solved up until 1978. This feat becomes even more impressive when one realizes that the number of variables in the standard formulation for this problem is approximately the square of the number of cities. Hence, we are talking about solving a problem with roughly 100 million variables.
There are several reasons for this impressive progress. Perhaps the most important is the dramatic increase in available computing power over the last decade, both in terms of processor speed and memory. This increase in the power of hardware has subsequently facilitated the development of increasingly sophisticated software for optimization, built on a wealth of theoretical results. As software development has become a central theme of optimization research efforts, many theoretical results have been “re-discovered” in light of their new-found computational importance. Finally, the use of parallel computing has allowed researchers to further leverage their gains.
Because of the rapidly increasing sophistication of computational techniques, one of the main difficulties faced by researchers who wish to apply these techniques is the level of effort required to develop an efficient implementation. The inherent need for incorporating problem-dependent methods (most notably for dynamic generation of variables and cutting planes) has typically required the time-consuming development of custom implementations. Around 1993, this led to the development by two independent research groups of software libraries aimed at providing a generic framework that users could easily customize for use in a particular problem setting. One of these groups, headed by Jünger and Thienel, eventually produced ABACUS (A Branch And CUt System) [21], while the other, headed by the authors, produced what was then known as COMPSys (Combinatorial Optimization Multi-processing System). After several revisions to enable more broad functionality, COMPSys became SYMPHONY (Single- or Multi-Process Optimization over Networks). A version of SYMPHONY written in C++, which we call COIN/BCP has also been produced at IBM under the COIN-OR project [24]. The COIN/BCP package takes substantially the same approach and has the same functionality as SYMPHONY, but has extended SYMPHONY's capabilities in some areas.