Let $G = (V, E)$ be a connected graph and let $M\subseteq V$. We say that a vertex $v$ is marked if $v\in M$. The problem is to find a simple path in $G$ that visits the maximum possible number of marked vertices. The associated decision problem is: is there a simple path that visits every $v\in M$?

The problem is obviously more general than the problem of finding a Hamiltonian path in an arbitrary graph, so it is NP-hard.

I see no obvious strategy; one can't simply disregard the unmarked vertices, since they (and their incident edges) may be part of the optimal path. Indeed, omitting them may disconnect the graph completely.

My questions:

  1. Does this problem have a well-known name?
  2. Are there any good approximation algorithms, heuristics, or simple reductions to problems I might know more about?
  3. Where can I find this problem discussed in the literature?
  • $\begingroup$ Have removed tag discussion; see here or a maybe upcoming new question on Computer Science Meta. $\endgroup$
    – Raphael
    Sep 20 '13 at 10:23

This problem is close to Steiner tree problem, just by two restriction:

  1. Instead of tree we have path.
  2. Is not necessary to have shortest path which contains required node.

There is a recent work1 on similar topic, the only difference with your problem statement is they are looking for shortest path which contains all required vertices. They name it Steiner Path problem.

1: Shanti Swaroop Moharana, Ankit Joshi, and Swapnil Vijay. Steiner path for trees. International Journal of Computer Applications, August 2013. Published by Foundation of Computer Science, New York, USA. (doi)

  • $\begingroup$ That paper seems to be only considering steiner paths on trees, not on general connected graphs, as the OP specified. $\endgroup$ Apr 21 '17 at 10:00

The nearest neighbor method is often used as a heuristic for different problems. Sometimes the method gives an effective approximation algorithm as well. You could employ it here as well. Here's a skeleton for a heuristic method that maintains a stack $S$ of vertices representing the resulting simple path. Let $F$ be a set containing forbidden vertices, that is, vertices that have already been used in our final path. $F$ should be a data structure supporting fast insertion and lookup, such as a hash table or even a bitvector.

  1. Pick a start node $s_0 \in M$ and let $S := \{s_0\}$, and $F = \emptyset$.
  2. Choose a successor $s_i$ to $\text{top}(S)$, and try to find a path $s_{i-1} \leadsto s_i$ avoiding vertices in $F$. If a path is found, insert the vertices on the path $s_{i-1} \leadsto s_i$ to $F$.
  3. Repeat step 2 until no successor can be found

For a more concrete implementation, consider the following:

  1. Let $\text{Per}_M(G)$ be the marked periphery, that is, the marked vertices with highest eccentricity. Pick the start node $s_0$ such that $s_0 \in \text{Per}_M(G)$.
  2. Using BFS, find the closest vertex to $\text{top}(S)$, where $S$ is the stack maintaining the path. Push the successor to the stack $S$. If a vertex $v$ is forbidden, i.e. $v \in F$, then the BFS won't visit it.
  3. Repeat step 2 until no successor can be found.

I'll argue for the concrete implementation above:

  1. You can think of picking the start vertex $s_0$ from the "convex hull" of $M$. Roughly, the idea is that you only need to expand the path into one direction, thus maximizing the "freedom of movement".
  2. BFS seems like the simplest idea; alternatively one could experiment with other search methods, such as bidirectional search or randomized DFS. A randomized DFS might be a bad choice when there's only a few elements in $F$, then it's possible it'll produce very long paths between two marked vertices. On the other hand, running a randomized DFS -based heuristic resembles the idea of random restarts, which has been very effective in say SAT/CSP solvers or local search methods.

If you are able to find any additional properties of your input graphs and/or $M$, it might very well help you in tuning this heuristic.


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