This is an algorithms question that I commonly ask of interview candidates.
You're given a list of songs (say, a list of numeric song ids). You're also given a scoring function that takes two songs, and then returns how good it sounds to listen to the second songs after listening to the first song. In general, this score is not commutative, i.e. the score of listening to song A after listening to song B is different from the score of listening to song B after listening to song A.
The task is to find the optimal playlist order to listen to the full list of songs and have the most goodness. The solution should be brute force, not a heuristic, and your program can have any running time.
This question is actually a variation of the Travelling Salesman Problem. The main differences are:
- in TSP, the starting city does not matter; in the mixtape variation, the first song on the playlist is significant
- in TSP you need to form a loop, i.e. return to the original city, whereas in the mixtape variation you end on the last song
The naive solution is to generate a list of all song permutations, score each permutation, and then return the list with the best permutation. This is fine, this solution has running time n! and correctly implementing a permutations function is tricky enough that a lot of people still don't correctly finish the problem after coming to this realization.
While this is the solution that 99% of the candidates I ask the question of identify, many (I would wager, most) do not realize that the solution they're implementing is equivalent to generating permutations (or say the word "permutations" in their response). It's my observation that having the insight that the brute force solution is equivalent to trying all possible song lists, and therefore is equivalent to generating all song permutations, significantly helps people compared to those who dive straight into a recursive depth-first search solution without making this insight.
The optimal solution has running time 2^n, and works by transforming the problem from permutations, to powerset. This is actually the "dynamic programming" solution of the problem. The idea is that we can work on building up solutions for smaller subsets of songs, first all pairs, then all triples, then all quadruples, etc. At each stage of buliding up the solution size N, we can re-use the optimal paths and distances for problems of the N-1 size. Because we have to generate all doubles, triples, quadruples, etc. this is equivalent to generating the powerset of the input song list (minus the empty set and size one sets).
Another advantage of this solution, in my opinion, is that this dynamic programming solution is naturally iterative and not recursive. It's absolutely possible to generate all of the song permutations without using recursion, but for some reason it seems much more difficult to most programmers.
The mixtape.py program provided implements this solution.