5 ms·
Though I agree to most of what you said, for learning algorithms you need not think about low level memory management. I had used CLRS many times to explain al
by ibuildthings 14y ago
Though I agree to most of what you said, for learning algorithms you need not think about low level memory management.
I had used CLRS many times to explain algorithms to non-CS students and collaborators ( who almost always preferred dynamically typed scripting languages like python and to be honest were scared of ints and floats ). At the same-time some of them were very good in algorithm design, and came up with clever (algorithmic) ways to solve the problem at hand much more efficiently.
- jasim 14y agoI had this experience when I was studying about Red-Black Trees. These are data structures made to make data access efficient. I'd argue that in such cases, it is important to limit the number of memory swaps and comparisons you make to keep the algorithm efficient. But your argument does hold true when one is dealing with things like sorting or graph traversal algorithms. There the concepts are most important and you can do well without worrying about the low level details.