How to compute optimal paths for traveling salesman bitonic tour?

Question

UPDATED

After more reading, the solution can be given with the following recurrence relation:

(a) When i = 1 and j = 2, l(i; j) = dist(pi; pj )
(b) When i < j - 1; l(i; j) = l(i; j - 1) + dist(pj-1; pj)
(c) When i = j - 1 and j > 2, min 1<=k<i (l(k; i) + dist(pk; pj ))

This is now starting to make sense, except for part C. How would I go about determining the minimum value k? I suppose it means you can iterate through all possible k values and just store the minimum result of ( l(k,i) + dist(pk,pj)?

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Yes, definitely a problem I was studying at school. We are studying bitonic tours for the traveling salesman problem.

Anyway, say I have 5 vertices {0,1,2,3,4}. I know my first step is to sort these in order of increasing x-coordinates. From there, I am a bit confused on how this would be done with dynamic programming.

I am reading that I should scan the list of sorted nodes, and maintain optimal paths for both parts (initial path and the return path). I am confused as to how I will calculate these optimal paths. For instance, how will I know if I should include a given node in the initial path or the return path, since it cannot be in both (except for the endpoints). Thinking back to Fibonacci in dynamic programming, you basically start with your base case and work your way forward. I guess what I am asking is how would I get started with the bitonic traveling salesman problem?

For something like the Fibonacci numbers, a dynamic programming approached is quite clear. However, I don't know if I am just being dense or what but I am quite confused trying to wrap my head around this problem.

Thanks for looking!

NOTE: I am not looking for complete solutions, but at least some good tips to get my started. For example, if this were the Fibonacci problem, one could illustrate how the first few numbers are calculated. Please let me know how I can improve the question as well.

Answer 1

Clarification on your algorithm.

The l(i,j) recursive function should compute the minimum distance of a bitonic tour i -> 1 -> j visiting all nodes that are smaller than i. So, the solution to the initial problem will be l(n,n)!

Important notes:

1. we can assume that the nodes are ordered by their x coordinate and labeled accordingly (p1.x < p2.x < p3.x ... < pn.x). It they weren't ordered, we could sort them in O(nlogn) time.

2. l(i,j) = l(j,i). The reason is that in the lhs, we have a i ->...-> 1 -> ... -> j tour which is optimal. However traversing this route backward will give us the same distance, and won't broke bitonic property.

Now the easy cases (note the changes!):

(a) When i = 1 and j = 2, l(i; j) = dist(pi; pj ) = dist(1,2)

Here we have the following tour : 1->1->...->2. Trivially this is equivalent to the length of the path 1->...->2. Since points are ordered by their .x coordinate, there is no point between 1 and 2, so the straight line connecting them will be the optimal one. ( Choosing any number of other points to visit before 2 would result in a longer path! )

(b) When i < j - 1; l(i; j) = l(i; j - 1) + dist(pj-1; pj)

In this case, j-1 must be on the part of the path 1 -> ... -> j, because the part i -> ... -> 1 can not contain nodes with an index bigger than i. Because all nodes in the path 1 -> ... -> j are in increasing order of index, there can be none between j-1 and j. So, this is equivalent to the tour: i -> ... -> 1 -> .... -> j-1 -> j, which is equivalent to l(i,j-1) + dist(pj-1,pj)!

Anf finally the interesting part comes:

(c) When i = j - 1 or i = j, min 1<=k<i (l(k; i) + dist(pk; pj ))

Here we know that we have to get from i to 1, but there is no clue on the backward sweep! The key idea here is that we must think of the node just before j on our backward route. It may be any of the nodes from 1 to j-1! Let us assume that this node is k. Now we have a tour: i -> ... -> 1 -> .... -> k -> j, right? The cost of this tour is l(i,k) + dist(pk,pj).

Hope you got it.

Implementation.

You will need a 2-dimensional array say BT[1..n][1..n]. Let i be the row index, j be the column index. How should we fill in this table?

In the first row we know BT[1][1] = 0, BT[1][2] = d(1,2), so we have only i,j indexes left that fall into the (b) category.

In the remainin rows, we fill the elements from the diagonal till the end.

Here is a sample C++ code (not tested):

void ComputeBitonicTSPCost( const std::vector< std::vector<int> >& dist, int* opt ) {
  int n = dist.size();
  std::vector< std::vector< int > > BT;
  BT.resize(n);
  for ( int i = 0; i < n; ++i )
    BT.at(i).resize(n);

  BT.at(0).at(0) = 0;  // p1 to p1 bitonic distance is 0
  BT.at(0).at(1) = dist.at(0).at(1);  // p1 to p2 bitonic distance is d(2,1)

  // fill the first row
  for ( int j = 2; j < n; ++j )
    BT.at(0).at(j) = BT.at(0).at(j-1) + dist.at(j-1).at(j);

  // fill the remaining rows
  int temp, min;  
  for ( int i = 1; i < n; ++i ) {
    for ( int j = i; j < n; ++j ) {
      BT.at(i).at(j) = -1;
      min = std::numeric_limits<int>::max();
      if ( i == j || i == j -1 ) {
        for( int k = 0; k < i; ++k ) {
          temp = BT.at(k).at(i) + dist.at(k).at(j);
          min = ( temp < min ) ? temp : min;
        }        
        BT.at(i).at(j) = min;        
      } else {
        BT.at(i).at(j) = BT.at(i).at(j-1) + dist.at(j-1).at(j);
      }
    }
  }

  *opt = BT.at(n-1).at(n-1);
}