I still remember the first time I stumbled upon mathematical optimization while studying economics.
It felt like unlocking a hidden superpower — here was a way to combine economics, production, energy, environmental impact, and so much more into a single decision-making model, all aimed at maximizing efficiency.
For me, the magic was never just in the math. It was in the application — taking abstract formulas and turning them into tools that actually shape industries, influence policy, and solve real-world problems.
Like many students a decade ago, my first step into optimization wasn’t with a fancy tool or a custom-built framework.
It was with something familiar: Excel’s Solver add-in.
At first, it felt like wizardry. I could:
But the magic didn’t last forever.
Soon, I ran into limits:
OpenSolver, an Excel add-in, helped make things more visual and flexible.
Still, as my models grew more complex, Excel became more of a bottleneck than a boost.
Next came SolverStudio, an Excel add-in that introduced me to the wider world of modeling languages.
That’s where I first met Pyomo and PuLP.
Here was the catch: to use them, I had to learn Python at the same time as learning the modeling frameworks.
For someone already grappling with optimization concepts, that was like trying to learn chess in a foreign language.
I realized I needed something that matched the logic of optimization — without the friction of excessive programming overhead.
I experimented with Julia, GAMS, and AMPL.
When I tried AMPL, it just… clicked.
My first impressions:
Everything ingenious is simple — and AMPL proved it.
Fast-forward to today, and AMPL still surprises me.
The development team keeps adding features that handle bigger challenges while keeping models readable and elegant.
Some personal highlights:
amplpy for the best of both worlds.I know many students and newcomers are right now where I was 10 years ago:
When I started, AMPL was harder to get. Today, free academic and personal licenses make it much more accessible.
Excel, Pyomo, PuLP, and others all have their place — but in my experience, AMPL has offered the best long-term mix of:
If you’re trying to choose a tool, here’s a side-by-side comparison table of AMPL, Pyomo, and PuLP based on my experience — so you can see how they stack up in problem scope, scalability, learning curve, and more.
Table legend
| Feature / Aspect | AMPL | Pyomo | PuLP |
|---|---|---|---|
| Source | Closed-source | Open-source | |
| Type of problem |
|
| Limited to LP/MILP |
| Model Size Scaling | Large | Medium | Low |
| License cost | |||
| – Academia | Free | Free | |
| – Research | |||
| – Personal | Free1 | ||
| – Enterprise | Paid2 | ||
| Ease of installation | Easy | ||
| Language Type, Syntax complexity and Readability | Multiple APIs, natural and concise syntax (high readability) | Python-based, flexible but verbose | LP-focused, but less expressive for complex problems |
| Integration |                     |
| Pure Python library |
| Development speed | Fastest | Slower for large models | Slower for large models |
| Solvers | Gurobi, CPLEX, Xpress, Mosek, COPT, CONOPT, Knitro, LOQO, SNOPT, MINOS, Baron, LGO, SNOPT, LINDO Global, Octeract, HiGHS, CBC, SCIP, GCG, IPOPT, Bonmin, Couenne, Gecode, cuOpt, RAPOSa | Gurobi, Ipopt, Cplex, Cbc, HiGHS, MAiNGO | Mosek, Gurobi, Cplex, Xpress, HiGHS, SCIP, XPRESS, COPT |
| Solvers support | Tight integration with solvers, providing consistent error reporting | Depends on the solver interface (some solvers give better feedback than others) | Minimal solver feedback; debugging is mostly manual |
| Performance (model gen time) | Fastest | Medium | Low–Medium |
| Debugging & diagnostics | |||
| Presolve phase | Yes | No | No |
| – Model Inspection & Debugging Tools |
| Requires manual inspection (e.g., model.pprint(), model.constraint_name.display()) | Very limited debugging tools (mostly printing variables/constraints) |
| – Handling Infeasibility & Unboundedness | Troubleshooting model infeasibility using AMPL’s presolve feature | No built-in infeasibility diagnostics; requires manual checking or solver-specific tools | No infeasibility debugging—mostly relies on solver reports |
| Community & support | |||
| – Technical support |
| Open-source community | Open-source community |
| – Forum | https://discuss.ampl.com/ | https://groups.google.com/g/pyomo-forum | https://github.com/coin-or/pulp/issues |
| – Books | The Pyomo Book | Yes | |
| – Online documentation | https://dev.ampl.com/index.html | Pyomo Documentation | https://coin-or.github.io/pulp/ |
| – Model repository | 100+ models | 10+ models | |
| Learning curve | Moderate (math-based+Python) | Steeper learning curve (+Python) | Steeper learning curve (+Python) |
This is just my story. I’d love to hear yours:
Drop your thoughts below — let’s make this a shared guide for anyone stepping into the world of optimization.
Technical Development Team