📈 To be more creative, visually-explained algorithms, finding the sweet spot
Local Optimum: short, imperfect-yet-useful ideas - Edition #14
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) 🌱 To be more creative…
Apply the 4R framework that I read the other day.
One of those Rs is…
Restrict: To create something, it’s often much better to think ‘inside a (small) box’ than to go on about ‘thinking outside the box.’ Although it may sound counterintuitive, constraints are a creator’s friends.
This is also true for businesses and optimization problems.
In logistics, what you CAN’T spend is often more important than what you CAN.
No budget cap? You’ll never weigh ROI.
No driver constraints? You’ll never optimize routes.
No order-mix limit? You’ll never cut warehouse time.
Constraints create optimization problems, what businesses care for, and make us more creative.
(and if you want to know what the other Rs mean, just reply to this email or leave a comment here)
2) 🖼️ Algorithms, visually explained
One thing I want to improve is how to visualize ideas.
It’s powerful.
Our brains process images up to 60,000 times faster than text, so a single graphic can land ideas in milliseconds what paragraphs never will.
Back in the wild, no one texted “lion ahead!”. We saw it and bolted. That split-second of visual processing still rules us today.
That’s why diagrams, flowcharts, and annotated sketches turn complex concepts into “aha!” moments.
If you want to see algorithms in practice, take a look here:
3) ⚖️ Finding the sweet spot
When tackling optimization problems, you need to evaluate what to use: an exact or a heuristic approach.
So think about…
💼 The business: will you be able to translate business needs into mathematical equations? Do the requirements change very frequently? Is it a large problem and you need a solution in a few seconds?
🧑🏻💻 Software development: you’ll need to debug and verify solutions, but also consider the programming language you’ll use.
🔢 Optimization: do you care about getting optimal solutions? About getting high-quality solutions in no time?
The choice of which algorithm to choose has many vertices and in most cases it is not usually a direct decision.
You need to understand the problem, the business needs and the skills of the team.
If you want more of this, read the full article:
Symmetry issues in optimization problems (part C)
Next Monday, I’ll share the rest of the article about symmetry issues in optimization problems.
In the first part, I talked about what they are and classical techniques to address them. In the second one, I talked about advanced techniques.
And in Part C, I’ll cover:
🔬 ML-guided techniques to break symmetries
🛠 Practical ways to dealing with it
I needed to split the article again, it was going to be a huge one!
If you’re dealing with symmetry problems and want to understand ML-guided techniques and useful tips, this will be useful. See you Monday!
And that’s it for today!
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Have a nice day ahead ☀️
Borja.
