Building optimization models in #Python too slow? Your loops are killing you. Loops in Python are executed in the interpreter, adding massive overhead. Here's what most data scientists miss: ❌ The slow way: for i in range(N): p.addConstraint(x[i] <= y[i]) ✅ The fast way: x = p.addVariables(N) y = p.addVariables(N) p.addConstraint(x <= y) The second approach eliminates the Python loop entirely. Other performance killers to avoid: 1) Multiple API calls instead of vectorized operations 2) Not using xp.Dot for multi-dimensional arrays 3) Forgetting scipy sparse matrices for large coefficient matrices Other basic model building best practices can be found in the link in the comments section. I've seen model build times drop from minutes to seconds just by applying these techniques. The math doesn't change. The decisions don't change. But your productivity skyrockets. FICO Xpress's Python API makes these optimizations natural and intuitive. Stop waiting for your models to build. Start coding smarter. What's your biggest Python performance bottleneck? #DataScience #Optimization #Coding #MachineLearning #DecisionIntelligence
We had a similar problem with our Python solver. Our API supported callbacks, for example: .for_each(Shift) .filter(lambda shift: shift.required_skill not in shift.employee.skills) .penalize("Missing required skill") The constraint code ran in non-interpreted language (Java in our case), which made it fast. Except the callback part (required_skill not in shift.employee.skills). The Python solver was 20x slower than our Java solver. Until we started transpiling the bytecode of those Python callbacks to Java. That made it 10x faster, so only 2x slower than Java. But that transpiling code quickly became unmaintanable... So much so that we even ended up freezing work on the Python solver, so our entire solver team can focus 100% on our open source Java solver... Mechanical Sympathy matters.
Hi Jay. Great post. I was wondering what is the constraint limit for the community license. Getting "invalid" errors and wondering if I am going overboard at the constraint limit. Thanks!!
Basic Python best practices: https://www.fico.com/fico-xpress-optimization/docs/latest/examples/python/GUID-F7F14C3D-DA4D-3AE2-BED8-53AB3D3D62F8.html