Automated Tuning of Optimization Software Parameters

We present a method to tune software parameters using ideas from software testing and machine learning. The method is based on the key observation that for many classes of instances, the software shows improved performance if a few critical parameters have “good” values, although which parameters are critical depends on the class of instances. Our … Read more

Visualizing Branch-and-Bound Algorithms

We present a suite of tools for visualizing the status and progress of branch-and-bound algorithms for mixed integer programming. By integrating these tools with the open-source codes CBC, SYMPHONY, and GLPK, we demonstrate the potential usefulness of visual representations in helping a user predict future progress of the algorithm or analyzing the algorithm’s performance. We … Read more