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Hello Reader! Ever get stuck on an optimization problem and wish you could get quick help without waiting for forum responses or digging through documentation for hours? Insights from the Pre-Summer-Break Conference Week The last week of June was busy in the optimization world. While many practitioners were traveling between conferences — Gurobi's events in Berlin and Amsterdam, plus the academic EURO conference in Leeds — there were some interesting developments worth sharing. Gurobi Launches the Gurobot During the busy conference week, Gurobi launched Gurobot, their optimization-focused AI assistant. The origins go back a couple of years. At the Barcelona Summit in 2023, I learned that some technical people at Gurobi had been experimenting with an LLM trained on their documentation. As a test, they used it to answer an actual support query from their forum. After the Gurobi experts team reviewed the response, they decided it was good enough to post as their official answer. After several rounds of refinement, Gurobot can now answer questions about Gurobi interfaces and even suggest complete optimization models. The integration allows for seamless creation of formal support tickets when you need human help, and it has context awareness about your Gurobi profile and license setup. How to Use Gurobot Effectively I tried it myself recently when I needed a simple example model for a client presentation. Instead of getting a textbook reference that would require hours of searching, Gurobi delivered exactly what I needed—a focused example demonstrating the specific issue I wanted to show. To get good results with Gurobot at gurobi.com (you just need a Gurobi account):
Andreas Wächter Joins Gurobi Andreas Wächter, the main developer behind the popular open-source nonlinear solver IPOPT, recently joined Gurobi's development team. With many US universities losing attraction for top talent, it makes sense that optimization experts are moving to where cutting-edge development happens. This will likely boost Gurobi's nonlinear optimization capabilities — both for local optimization (which IPOPT handles well) and global optimization, since global techniques use local optimization internally. Time to Dream If you had a magic wand, what kind of nonlinear problem would you love to see solved instantly? Hit reply and let me know — I'll also share what my first gut instinct told me. Until the next iteration! Tim Varelmann Complicated Decisions - Simply Automated! Follow me on LinkedIn P.S. If you try Gurobot, I'd be curious to hear how it compares to your usual approach for getting optimization help. |
I write about my everyday life as optimization expert, where I translate business requirements to mathematical formulars, then to software -- and all the way back again.
Hello Reader! Optimization is one of the most powerful technologies of our time — and yet, it rarely makes headlines. I'm currently returning from the Gurobi Summit 2025 in Vienna. Thus, this newsletter will be a little different: let’s start with three quotes worth highlighting from the event: My personal Top-3 of Quotes “Two years from now, spam will be solved.” – Bill Gates, 2004 This quote was shown at the Summit, alongside many others from world-class figures. And it stuck with me. Not...
Hello Reader! with wind and rain becoming ever-present, summer has officially given way to autumn. In this episode of Bluebird Briefings I'll give you a little summary of what happened in the world of optimization over the summer of 2025: Major Industry News Gurobi released the State of Mathematical Optimization 2025 report. It contains results from what's probably the biggest survey among optimization practitioners. The findings are impressive. They attest to the growth of optimization as...
Hello Reader! Every Superhero Has an Origin Story For GAMSPy’s machine learning superpowers, that story begins with a package called OMLT. OMLT is short for Optimization and Machine Learning Toolkit. It’s a package that extends Pyomo to integrate machine learning models directly into optimization problems. Because OMLT supports ONNX interoperability, you can take a model trained in TensorFlow, Keras, or PyTorch and embed it into a Pyomo optimization model. It also happens to be the package...