- While DP might be rare, memoize is quite common alternative. Sticking a @functools.cache in Python for example. They are functionally equivalent in many cases,
- I believe knowing their data-structures and algorithms is the difference between an okay programmer and a good one,
- When interviewing entry-level software-engineers, there's not much else you can ask them except for what they learned in class, which is these kinds of questions.
For me, the difference between and okay programmer and a good one is: how much Linux/Unix they know (processes, core system calls, networking, sockets, awk/sed/bash). Whether you are writing apis for ecommerce platforms or writing custom k8s providers, Linux knowledge is a must. Knowing how to implement a trie? I bet most of us have never been in that situation.
Likewise, there are jobs where being able to reason on complexity and data structures is important.
They're not just functionally equivalent. They are the same thing. There are two things you need for a dynamic programming solution:
1. You remember the answer every time your function is called. ("Memoization".)
2. You structure your function calls so that they will ask for a lot of answers that have already been computed.
What you don't want is: say you're computing the sums of contiguous subsections of an array. You've already figured out that indices [2:5] sum to 3 and indices [5:8] sum to 8. When you need the sum of indices [2:8], you need to make sure you get it by asking for sum(2,5)+sum(5,8), and not by asking for sum(2,6)+sum(6,8).
At a high level they are the same, but it makes sense to distinguish between the techniques based on whether you need random access to the already-computed data.
For example, if you are computing the nth fibonacci number f(n), a DP solution needs only the state f(n-1) and f(n), but a typical memoized solution keeps around all the state f(1) through f(n-1). For tougher problems, you can't always convert between them trivially while keeping performance the same.
That's all true, but is it comparing like with like?
The "generic DP" solution to the problem is to allocate an array of n elements and fill it in from f(1) to f(n). That's the same amount of storage the "typical" memoized solution uses; cutting it down to an array of 2 elements plus an (implicit) offset between the part of the array that's in use and the full array seems to be an optimization on top of the DP solution.
You could apply the same optimization to a memoized form of the solution. I certainly agree that you most likely wouldn't do that, but you could.
What do we learn by comparing a tightly optimized DP solution against a "typical" memoized solution? Why do we give the DP solution credit for not using what it "doesn't need" while penalizing the memoized solution for what the programmer probably added unnecessarily?
Your broad strokes don’t account for the domain knowledge of a job. If all you’re doing is interviewing college grads, then I guess you have no choice, but this interview question sucks in just about any other context.
