Longest increasing subsequence


Question

Given an input sequence, what is the best way to find the longest (not necessarily continuous) non-decreasing subsequence.

0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3, 11, 7, 15 # sequence

1, 9, 13, 15 # non-decreasing subsequence

0, 2, 6, 9, 13, 15 # longest non-deceasing subsequence (not unique)

I'm looking for the best algorithm. If there is code, Python would be nice, but anything is alright.

1
31
10/21/2010 11:04:17 PM

Accepted Answer

I just stumbled in this problem, and came up with this Python 3 implementation:

def subsequence(seq):
    if not seq:
        return seq

    M = [None] * len(seq)    # offset by 1 (j -> j-1)
    P = [None] * len(seq)

    # Since we have at least one element in our list, we can start by 
    # knowing that the there's at least an increasing subsequence of length one:
    # the first element.
    L = 1
    M[0] = 0

    # Looping over the sequence starting from the second element
    for i in range(1, len(seq)):
        # Binary search: we want the largest j <= L
        #  such that seq[M[j]] < seq[i] (default j = 0),
        #  hence we want the lower bound at the end of the search process.
        lower = 0
        upper = L

        # Since the binary search will not look at the upper bound value,
        # we'll have to check that manually
        if seq[M[upper-1]] < seq[i]:
            j = upper

        else:
            # actual binary search loop
            while upper - lower > 1:
                mid = (upper + lower) // 2
                if seq[M[mid-1]] < seq[i]:
                    lower = mid
                else:
                    upper = mid

            j = lower    # this will also set the default value to 0

        P[i] = M[j-1]

        if j == L or seq[i] < seq[M[j]]:
            M[j] = i
            L = max(L, j+1)

    # Building the result: [seq[M[L-1]], seq[P[M[L-1]]], seq[P[P[M[L-1]]]], ...]
    result = []
    pos = M[L-1]
    for _ in range(L):
        result.append(seq[pos])
        pos = P[pos]

    return result[::-1]    # reversing

Since it took me some time to understand how the algorithm works I was a little verbose with comments, and I'll also add a quick explanation:

  • seq is the input sequence.
  • L is a number: it gets updated while looping over the sequence and it marks the length of longest incresing subsequence found up to that moment.
  • M is a list. M[j-1] will point to an index of seq that holds the smallest value that could be used (at the end) to build an increasing subsequence of length j.
  • P is a list. P[i] will point to M[j], where i is the index of seq. In a few words, it tells which is the previous element of the subsequence. P is used to build the result at the end.

How the algorithm works:

  1. Handle the special case of an empty sequence.
  2. Start with a subsequence of 1 element.
  3. Loop over the input sequence with index i.
  4. With a binary search find the j that let seq[M[j] be < than seq[i].
  5. Update P, M and L.
  6. Traceback the result and return it reversed.

Note: The only differences with the wikipedia algorithm are the offset of 1 in the M list, and that X is here called seq. I also test it with a slightly improved unit test version of the one showed in Eric Gustavson answer and it passed all tests.


Example:

seq = [30, 10, 20, 50, 40, 80, 60]

       0    1   2   3   4   5   6   <-- indexes

At the end we'll have:

M = [1, 2, 4, 6, None, None, None]
P = [None, None, 1, 2, 2, 4, 4]
result = [10, 20, 40, 60]

As you'll see P is pretty straightforward. We have to look at it from the end, so it tells that before 60 there's 40,before 80 there's 40, before 40 there's 20, before 50 there's 20 and before 20 there's 10, stop.

The complicated part is on M. At the beginning M was [0, None, None, ...] since the last element of the subsequence of length 1 (hence position 0 in M) was at the index 0: 30.

At this point we'll start looping on seq and look at 10, since 10 is < than 30, M will be updated:

if j == L or seq[i] < seq[M[j]]:
    M[j] = i

So now M looks like: [1, None, None, ...]. This is a good thing, because 10 have more chanches to create a longer increasing subsequence. (The new 1 is the index of 10)

Now it's the turn of 20. With 10 and 20 we have subsequence of length 2 (index 1 in M), so M will be: [1, 2, None, ...]. (The new 2 is the index of 20)

Now it's the turn of 50. 50 will not be part of any subsequence so nothing changes.

Now it's the turn of 40. With 10, 20 and 40 we have a sub of length 3 (index 2 in M, so M will be: [1, 2, 4, None, ...] . (The new 4 is the index of 40)

And so on...

For a complete walk through the code you can copy and paste it here :)

29
5/23/2017 12:10:37 PM

Here is how to simply find longest increasing/decreasing subsequence in Mathematica:

 LIS[list_] := LongestCommonSequence[Sort[list], list];
 input={0, 8, 4, 12, 2, 10, 6, 14, 1, 9, 5, 13, 3, 11, 7, 15};
 LIS[input]
 -1*LIS[-1*input]

Output:

{0, 2, 6, 9, 11, 15}
{12, 10, 9, 5, 3}

Mathematica has also LongestIncreasingSubsequence function in the Combinatorica` libary. If you do not have Mathematica you can query the WolframAlpha.

C++ O(nlogn) solution

There's also an O(nlogn) solution based on some observations. Let Ai,j be the smallest possible tail out of all increasing subsequences of length j using elements a1, a2, ... , ai. Observe that, for any particular i, Ai,1, Ai,2, ... , Ai,j. This suggests that if we want the longest subsequence that ends with ai + 1, we only need to look for a j such that Ai,j < ai + 1 < = Ai,j + 1 and the length will be j + 1. Notice that in this case, Ai + 1,j + 1 will be equal to ai + 1, and all Ai + 1,k will be equal to Ai,k for k!=j+1. Furthermore, there is at most one difference between the set Ai and the set Ai + 1, which is caused by this search. Since A is always ordered in increasing order, and the operation does not change this ordering, we can do a binary search for every single a1, a2, ... , an.

Implementation C++ (O(nlogn) algorithm)

#include <vector>
using namespace std;

/* Finds longest strictly increasing subsequence. O(n log k) algorithm. */
void find_lis(vector<int> &a, vector<int> &b)
{
  vector<int> p(a.size());
  int u, v;

  if (a.empty()) return;

  b.push_back(0);

  for (size_t i = 1; i < a.size(); i++) {
      if (a[b.back()] < a[i]) {
          p[i] = b.back();
          b.push_back(i);
          continue;
      }

      for (u = 0, v = b.size()-1; u < v;) {
          int c = (u + v) / 2;
          if (a[b[c]] < a[i]) u=c+1; else v=c;
      }

      if (a[i] < a[b[u]]) {
          if (u > 0) p[i] = b[u-1];
          b[u] = i;
      }   
  }

  for (u = b.size(), v = b.back(); u--; v = p[v]) b[u] = v;
}

/* Example of usage: */
#include <cstdio>
int main()
{
  int a[] = { 1, 9, 3, 8, 11, 4, 5, 6, 4, 19, 7, 1, 7 };
  vector<int> seq(a, a+sizeof(a)/sizeof(a[0]));
  vector<int> lis;
        find_lis(seq, lis);

  for (size_t i = 0; i < lis.size(); i++)
      printf("%d ", seq[lis[i]]);
        printf("\n");    

  return 0;
}

Source: link

I have rewritten the C++ implementation to Java a while ago, and can confirm it works. Vector alternative in python is List. But if you want to test it yourself, here is link for online compiler with example implementation loaded: link

Example data is: { 1, 9, 3, 8, 11, 4, 5, 6, 4, 19, 7, 1, 7 } and answer: 1 3 4 5 6 7.


Licensed under: CC-BY-SA with attribution
Not affiliated with: Stack Overflow
Icon