📈 What do you need to leave a local maximum? + 3 other interesting insights
Search space: your dose of OR discoveries
Hey, Borja here! 👋
As you know, I'm trying something new.
Instead of letting all the interesting research and tools I discover each week disappear into my bookmark graveyard, I'm sharing the ones that made me pause and think this is clever or I need to remember this.
Consider it your dose of OR discoveries without the time investment of finding them yourself.
Let me know if this hits the mark: your feedback will determine if this becomes a regular thing.
📚 The Polymathic Engineer is a newsletter that talks about algorithms. I found it on LinkedIn as the creator shared how to get better at algorithms and data structures. Via Fernando Franco.
🧗♂️ If you want to leave a local maximum, you have to go down, so this technique seems to be effective. Via Benjamin Doerr.
🧭 Moving from strategic to tactical to operational decisions is not that easy, even when the problem resembles from one layer to the other. Sometimes you get infeasibilities or poor solutions. PAMSO comes to the rescue in that part. Via Can Li.
⚙️ MCP seems to be the de-facto standard to communicate with ChatGPT-like apps. What if we can wrap a solver like HiGHS with MCP so we can build models in plain English? It has its own caveats, but these approaches make sense in today’s world. Via Wilfred Springer.
Found something I should see? Send it my way and I’ll credit you next week.
In the meantime…
Let’s keep optimizing,
Borja.
