Author here.
Sorry, If it feels misleading. I have no intention to mislead anybody. If you can suggest alternative title, I am happy to change title.
Just a side note: with Fibonacci + caching it solidly become Dynamic programming problem so Time complexity reduces from quadratic to o(n), IIRC .
There is a whole class of problems where recursion + memoization(caching) = Top Down Dynamic programming , The other way to Increase performance and actually reduce call stack in these class of problems including Fibonacci would be Bottom Up Dynamic Programming
Some gists I found on it https://gist.github.com/trtg/4662449
The follow-up posts go into even more detail on individual problems that can be solved using either form of dynamic programming, including some real-world problems like content-aware image resizing.
Most of my writing can be found at my blog (https://avikdas.com/), but here are some from the same dynamic programming series:
- Deep-dive into the Chain Matrix Multiplication Problem - https://avikdas.com/2019/04/25/dynamic-programming-deep-dive... - Real-world applications of DP: https://avikdas.com/2019/05/14/real-world-dynamic-programmin... and https://avikdas.com/2019/07/29/improved-seam-carving-with-fo... - Another real-world application, this time in machine learning - https://avikdas.com/2019/06/24/dynamic-programming-for-machi...
If you look on my blog, you'll also see my recent series is on scalability, things like read-after-write consistency and queues for reliability.
Edit: I don't actually have much of a problem with the article headline as it is now. It depends a lot on your target audience! For Hacker News, yeah, we know what memoization is because we learned it in CS 102, right after we learned about recursion.
But for a lot of people who would get the most value from the article, the word "memoization" isn't going to mean much, and wouldn't read the article.
Maybe something like: "Memoization in Python, or how I learned to speed up function calls in just one line"