I'd argue the point of the exercise isn't to implement an O(1) solution, but to verify that the applicant understands how the logic they're writing actually executes, and is capable of minimizing complexity.

Whether they arrive at an O(1) solution doesn't matter a bit to me. Most of the time, I don't even care if they phrase it in big-O notation - or have even heard the term!

Given the choice between someone who completed the exercise with an O(1) solution in half the allotted time and someone who never wrote a single line of valid code, I'll choose the person who is able to articulate the problem and potential solutions best every time.

It's far easier to search the web and find a "cute solution" than it is to learn how to think about and communicate complexity.

> But the whole point of the "cute solution" is to solve with O(1) memory; if you're willing to allow O(n) then the linked list also admits more sane solutions.

Yeah but the "parallel BFS" solution is actually slower. Overall people sacrifice memory for speed in these algorithm problems.

Additionally the input is already O(N) so if you count the input as memory the addition of O(2N) doesn't shift it. If you don't count it well you're deliberately removing real world actuality and making the problem harder.

>I'm not sure what you mean by comparing "layers" of traversal. Tortoise and hare is about provide a termination condition in the case that a cycle exists. In the case that there's no cycle, every algorithm must inspect each node, so is O(n). In both the linked list and the DAG case, if there is at least one cycle, (a) both pointers will enter it in at most O(n) time, and they will match in at most O(n) additional time, so this stays big-oh optimal.

It's layer based traversal via BFS. You have to compare the layers produced by the tortoise and the hare. Let's see for a binary tree:

  For traversal: 
  hare: O(N)
  tortoise: O(N)
  There are N total nodes so traversal via bfs or dfs will always be N.

BFS traversal happens in layers of breadth. The height of a full binary tree is log2(N + 1) - 1. The length of the last layer is 2^height. So the amount of nodes in the last layer is (N+1)/2

Because at each stage of traversal from the turtle and the hare we have to compare the "layer" of traversal between the hare and the tortoise to see if they overlap there is a roughly O(((N+1)/2 )^ 2) comparison as we cycle through each node in the layer to see if it exists in the other layer being compared. This reduces to O(N^2) processing time.

So total is O(N^3) because you have O(N) traversal time, and O(N^2) comparison time on each traversal step. You can reduce the layer comparison to O(N) if you save one layer to a hashset and then compare that set to the other layer, but that has a memory cost and only reduces the total big O to O(N^2).

I forgot to mention, BFS requires a queue. So if the graph is size N and with one root node and all the rest of the nodes as children that queue will go up to size N. So your solution doesn't actually get away from the memory issue at all.