The performance of mixed-integer programming solvers is subject to some unexpected variability that appears, for example, when changing from one computing platform to another, when permuting rows and/or columns of a model, when adding seemingly neutral changes to the solution process, etc. This phenomenon has been observed for decades, but only recently has it started to be methodologically analyzed with the two possible aims of either reducing or exploiting it, ideally both. In this tutorial we discuss the roots of performance variability, we provide useful tips to recognize it, and we point out some severe misinterpretations that might be generated by not performing/analyzing benchmark results carefully. Finally, we report on the most recent attempts to gain from variability.
Performance Variability in Mixed-Integer Programming
LODI, ANDREA;
2013
Abstract
The performance of mixed-integer programming solvers is subject to some unexpected variability that appears, for example, when changing from one computing platform to another, when permuting rows and/or columns of a model, when adding seemingly neutral changes to the solution process, etc. This phenomenon has been observed for decades, but only recently has it started to be methodologically analyzed with the two possible aims of either reducing or exploiting it, ideally both. In this tutorial we discuss the roots of performance variability, we provide useful tips to recognize it, and we point out some severe misinterpretations that might be generated by not performing/analyzing benchmark results carefully. Finally, we report on the most recent attempts to gain from variability.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
