For this project, my end goal is to interface a series of Sympy equations that I've built to an optimization solver. In the context of optimization, binary variables usually represent a decision, and in the context of solving, they represent a "branch" point. Branch points make it expensive to find a solution because each branch is a basically a copy of the problem that must be individually solved, and most implementations keep /both/ sub problems in memory. For large optimization problems (on the order of millions of variables and millions of equations), even a reduction of a single variable or equation can mean huge gains in solve-time.
I'm but human, and the equations I have written may have more room for symbolic simplification before I pass them to the solver; this is where I'm hoping Sympy can help. I'll take a look at using the Mod() operation, perhaps in it's own pass of the system for each equation that has a binary variable. If you're curious and don't already know about it, here's some "gentle reading" in regards to mathematical optimization. Chapters 12 and specifically 13 would be of pertinence. Practical Optimization: A Gentle Introduction<http://www.sce.carleton.ca/faculty/chinneck/po.html> Thanks for the pointer to Mod(). -- You received this message because you are subscribed to the Google Groups "sympy" group. To view this discussion on the web visit https://groups.google.com/d/msg/sympy/-/WbtZDllULS0J. To post to this group, send email to sympy@googlegroups.com. To unsubscribe from this group, send email to sympy+unsubscr...@googlegroups.com. For more options, visit this group at http://groups.google.com/group/sympy?hl=en.
