So, I’ve been diving into some interesting coding challenges lately and stumbled upon this one involving matrices that got me thinking. The goal is pretty simple, but it has a fun twist. Imagine you have a rectangular matrix filled with integers, and your task is to find the index of the row that has the most non-zero elements. If it turns out two rows have the same maximum number of non-zero elements, you need to pick the one with the smaller index.

Here’s the catch: I’ve been trying to determine the best way to implement this efficiently. Let’s say we have a matrix like this:

“`
[
[1, 0, 0, 3],
[0, 0, 0, 0],
[4, 5, 0, 6],
[7, 0, 0, 0],
[0, 2, 3, 4]
]
“`

In this case, the second last row (index 4) has three non-zero elements, which is the highest count in the matrix. The other rows either have fewer non-zero elements or are completely zeroed out. It’s important to loop through each row, count those non-zero entries, and then figure out which one has the highest count.

But I feel like there must be some clever shortcuts or optimizations to make this even faster or more elegant. For instance, should I use any specific programming paradigms like recursion or perhaps a more functional approach? What kind of data structures would be beneficial here?

Also, how would you handle larger matrices? Say, millions of rows or columns. How would you ensure your solution scales well without getting bogged down by performance issues?

I’m curious to hear how others would tackle this problem. Are there any specific languages or techniques you think would shine in this scenario? Would love to get your thoughts or even see some example code!