📈 Predicting runtimes, others on this, the true driver of solver speed
Local Optimum: short, imperfect-yet-useful ideas - Edition #23
Welcome to a new edition of Local Optimum: a short, imperfect-yet-useful collection of ideas related to optimization, decision-making, and applied Operations Research.
Let’s dive in! 🪂
1) ⌛ Predicting runtimes
Yesterday I was debating with my wife about predicting running times in optimization problems.
(She’s a physicist working as Data Scientist, meaning she’s a very analytical person + know about Machine Learning algorithms, and has more optimization knowledge than the average person.)
Anyway.
I argued it’s a super difficult task, if not impossible, due to:
the structure of the problem,
parameterization,
and even what you define as “solved”
…make it a moving target.
2) 🤔 Others on this
Curious as I am, I went digging and found an interesting r/optimization thread:
(click on the image below to go to the source)
3) 🏎️ The true driver of solver speed
Here’s a question I love to ask:
Would you rather solve a MIP with today’s solvers on a 1991 machine?
Or with 1991 solvers on today’s machine?
In other words: what matters more, hardware or software?
Here’s why I think software is the true driver:
A short overview of the most impactful surveys around optimization
Next Monday, I’ll share my take on two recent optimization surveys, including the State of Mathematical Optimization 2025.
I’ll cover 📌 what’s in them for you as an OR practitioner and 🧭 strategic implications.
If you care about how our work is evolving, you’ll want to read that one.
See you Monday!
PS: take a look at my first impressions to the Mathematical Optimization report.
And that’s it for today!
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Have a nice day ahead ☀️
Borja.
