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Longest Common Extension / LCE using Segment Tree

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Prerequisites : LCE(Set 1), LCE(Set 2), Suffix Array (n Log Log n), Kasai’s algorithm and Segment Tree

The Longest Common Extension (LCE) problem considers a string s and computes, for each pair (L , R), the longest sub string of s that starts at both L and R. In LCE, in each of the query we have to answer the length of the longest common prefix starting at indexes L and R.

String : “abbababba”
Queries: LCE(1, 2), LCE(1, 6) and LCE(0, 5)

Find the length of the Longest Common Prefix starting at index given as, (1, 2), (1, 6) and (0, 5).

The string highlighted “green” are the longest common prefix starting at index- L and R of the respective queries. We have to find the length of the longest common prefix starting at index- (1, 2), (1, 6) and (0, 5).

Longest Common Extension

In this set we will discuss about the Segment Tree approach to solve the LCE problem.

In Set 2, we saw that an LCE problem can be converted into a RMQ problem.

To process the RMQ efficiently, we build a segment tree on the lcp array and then efficiently answer the LCE queries.

To find low and high, we must have to compute the suffix array first and then from the suffix array we compute the inverse suffix array.

We also need lcp array, hence we use Kasai’s Algorithm to find lcp array from the suffix array.

Once the above things are done, we simply find the minimum value in lcp array from index low to high (as proved above) for each query.

Without proving we will use the direct result (deduced after mathematical proofs)-

LCE (L, R) = RMQlcp(invSuff[R], invSuff[L]-1)

The subscript lcp means that we have to perform RMQ on the lcp array and hence we will build a segment tree on the lcp array.

// A C++ Program to find the length of longest common
// extension using Segment Tree
using namespace std;
// Structure to represent a query of form (L,R)
struct Query
    int L, R;
// Structure to store information of a suffix
struct suffix
    int index;  // To store original index
    int rank[2]; // To store ranks and next rank pair
// A utility function to get minimum of two numbers
int minVal(int x, int y)
    return (x < y)? x: y;
// A utility function to get the middle index from
// corner indexes.
int getMid(int s, int e)
    return s + (e -s)/2;
/*  A recursive function to get the minimum value
    in a given range of array indexes. The following
    are parameters for this function.
    st    --> Pointer to segment tree
    index --> Index of current node in the segment
              tree. Initially 0 is passed as root
              is always at index 0
    ss & se  --> Starting and ending indexes of the
                  segment represented by current
                  node, i.e., st[index]
    qs & qe  --> Starting and ending indexes of query
                 range */
int RMQUtil(int *st, int ss, int se, int qs, int qe,
                                           int index)
    // If segment of this node is a part of given range,
    // then return the min of the segment
    if (qs <= ss && qe >= se)
        return st[index];
    // If segment of this node is outside the given range
    if (se < qs || ss > qe)
        return INT_MAX;
    // If a part of this segment overlaps with the given
    // range
    int mid = getMid(ss, se);
    return minVal(RMQUtil(st, ss, mid, qs, qe, 2*index+1),
                  RMQUtil(st, mid+1, se, qs, qe, 2*index+2));
// Return minimum of elements in range from index qs
// (query start) to qe (query end).  It mainly uses RMQUtil()
int RMQ(int *st, int n, int qs, int qe)
    // Check for erroneous input values
    if (qs < 0 || qe > n-1 || qs > qe)
        printf("Invalid Input");
        return -1;
    return RMQUtil(st, 0, n-1, qs, qe, 0);
// A recursive function that constructs Segment Tree
// for array[]. si is index of current node in
// segment tree st
int constructSTUtil(int arr[], int ss, int se, int *st,
                                               int si)
    // If there is one element in array, store it in
    // current node of segment tree and return
    if (ss == se)
        st[si] = arr[ss];
        return arr[ss];
    // If there are more than one elements, then recur
    // for left and right subtrees and store the minimum
    // of two values in this node
    int mid = getMid(ss, se);
    st[si] =  minVal(constructSTUtil(arr, ss, mid, st, si*2+1),
                  constructSTUtil(arr, mid+1, se, st, si*2+2));
    return st[si];
/* Function to construct segment tree from given array.
   This function allocates memory for segment tree and
   calls constructSTUtil() to fill the allocated memory */
int *constructST(int arr[], int n)
    // Allocate memory for segment tree
    //Height of segment tree
    int x = (int)(ceil(log2(n)));
    // Maximum size of segment tree
    int max_size = 2*(int)pow(2, x) - 1;
    int *st = new int[max_size];
    // Fill the allocated memory st
    constructSTUtil(arr, 0, n-1, st, 0);
    // Return the constructed segment tree
    return st;
// A comparison function used by sort() to compare
// two suffixes Compares two pairs, returns 1 if
// first pair is smaller
int cmp(struct suffix a, struct suffix b)
    return (a.rank[0] == b.rank[0])?
           (a.rank[1] < b.rank[1] ?1: 0):
           (a.rank[0] < b.rank[0] ?1: 0);
// This is the main function that takes a string
// 'txt' of size n as an argument, builds and return
// the suffix array for the given string
vector<int> buildSuffixArray(string txt, int n)
    // A structure to store suffixes and their indexes
    struct suffix suffixes[n];
    // Store suffixes and their indexes in an array
    // of structures. The structure is needed to sort
    // the suffixes alphabetically and maintain their
    // old indexes while sorting
    for (int i = 0; i < n; i++)
        suffixes[i].index = i;
        suffixes[i].rank[0] = txt[i] - 'a';
        suffixes[i].rank[1] = ((i+1) < n)?
                            (txt[i + 1] - 'a'): -1;
    // Sort the suffixes using the comparison function
    // defined above.
    sort(suffixes, suffixes+n, cmp);
    // At his point, all suffixes are sorted according to first
    // 2 characters.  Let us sort suffixes according to first 4
    // characters, then first 8 and so on
    int ind[n];  // This array is needed to get the index
                 // in suffixes[]
    // from original index.  This mapping is needed to get
    // next suffix.
    for (int k = 4; k < 2*n; k = k*2)
        // Assigning rank and index values to first suffix
        int rank = 0;
        int prev_rank = suffixes[0].rank[0];
        suffixes[0].rank[0] = rank;
        ind[suffixes[0].index] = 0;
        // Assigning rank to suffixes
        for (int i = 1; i < n; i++)
            // If first rank and next ranks are same as
            // that of previous suffix in array, assign
            // the same new rank to this suffix
            if (suffixes[i].rank[0] == prev_rank &&
              suffixes[i].rank[1] == suffixes[i-1].rank[1])
                prev_rank = suffixes[i].rank[0];
                suffixes[i].rank[0] = rank;
            else // Otherwise increment rank and assign
                prev_rank = suffixes[i].rank[0];
                suffixes[i].rank[0] = ++rank;
            ind[suffixes[i].index] = i;
        // Assign next rank to every suffix
        for (int i = 0; i < n; i++)
            int nextindex = suffixes[i].index + k/2;
            suffixes[i].rank[1] = (nextindex < n)?
                  suffixes[ind[nextindex]].rank[0]: -1;
        // Sort the suffixes according to first k characters
        sort(suffixes, suffixes+n, cmp);
    // Store indexes of all sorted suffixes in the suffix array
    for (int i = 0; i < n; i++)
    // Return the suffix array
    return  suffixArr;
/* To construct and return LCP */
vector<int> kasai(string txt, vector<int> suffixArr,
                              vector<int> &invSuff)
    int n = suffixArr.size();
    // To store LCP array
    vector<int> lcp(n, 0);
    // Fill values in invSuff[]
    for (int i=0; i < n; i++)
        invSuff[suffixArr[i]] = i;
    // Initialize length of previous LCP
    int k = 0;
    // Process all suffixes one by one starting from
    // first suffix in txt[]
    for (int i=0; i<n; i++)
        /* If the current suffix is at n-1, then we don?t
           have next substring to consider. So lcp is not
           defined for this substring, we put zero. */
        if (invSuff[i] == n-1)
            k = 0;
        /* j contains index of the next substring to
           be considered  to compare with the present
           substring, i.e., next string in suffix array */
        int j = suffixArr[invSuff[i]+1];
        // Directly start matching from k'th index as
        // at-least k-1 characters will match
        while (i+k<n && j+k<n && txt[i+k]==txt[j+k])
        lcp[invSuff[i]] = k; // lcp for the present suffix.
        // Deleting the starting character from the string.
        if (k>0)
    // return the constructed lcp array
    return lcp;
// A utility function to find longest common extension
// from index - L and index - R
int LCE(int *st, vector<int>lcp, vector<int>invSuff,
        int n, int L, int R)
    // Handle the corner case
    if (L == R)
        return (n-L);
    // Use the formula  -
    // LCE (L, R) = RMQ lcp (invSuff[R], invSuff[L]-1)
    return (RMQ(st, n, invSuff[R], invSuff[L]-1));
// A function to answer queries of longest common extension
void LCEQueries(string str, int n, Query q[],
                int m)
    // Build a suffix array
    vector<int>suffixArr = buildSuffixArray(str, str.length());
    // An auxiliary array to store inverse of suffix array
    // elements. For example if suffixArr[0] is 5, the
    // invSuff[5] would store 0.  This is used to get next
    // suffix string from suffix array.
    vector<int> invSuff(n, 0);
    // Build a lcp vector
    vector<int>lcp = kasai(str, suffixArr, invSuff);
    int lcpArr[n];
    // Convert to lcp array
    for (int i=0; i<n; i++)
        lcpArr[i] = lcp[i];
    // Build segment tree from lcp array
    int *st = constructST(lcpArr, n);
    for (int i=0; i<m; i++)
        int L = q[i].L;
        int R = q[i].R;
        printf("LCE (%d, %d) = %d\n", L, R,
             LCE(st, lcp, invSuff, n, L, R));
// Driver Program to test above functions
int main()
    string str = "abbababba";
    int n = str.length();
    // LCA Queries to answer
    Query q[] = {{1, 2}, {1, 6}, {0, 5}};
    int m = sizeof(q)/sizeof(q[0]);
    LCEQueries(str, n, q, m);
    return (0);


LCE (1, 2) = 1
LCE (1, 6) = 3
LCE (0, 5) = 4

Time Complexity : To construct the lcp and the suffix array it takes O(N.logN) time. To answer each query it takes O(log N). Hence the overall time complexity is O(N.logN + Q.logN)). Although we can construct the lcp array and the suffix array in O(N) time using other algorithms.
Q = Number of LCE Queries.
N = Length of the input string.

Auxiliary Space :
We use O(N) auxiliary space to store lcp, suffix and inverse suffix arrays and segment tree.

Comparison of Performances: We have seen three algorithm to compute the length of the LCE.

Set 1 : Naive Method [O(N.Q)]
Set 2 : RMQ-Direct Minimum Method [O(N.logN + Q. (|invSuff[R] – invSuff[L]|))]
Set 3 : Segment Tree Method [O(N.logN + Q.logN))]

invSuff[] = Inverse suffix array of the input string.

From the asymptotic time complexity it seems that the Segment Tree method is most efficient and the other two are very inefficient.

But when it comes to practical world this is not the case. If we plot a graph between time vs log((|invSuff[R] – invSuff[L]|) for typical files having random strings for various runs, then the result is as shown below.

The above graph is taken from this reference. The tests were run on 25 files having random strings ranging from 0.7 MB to 2 GB. The exact sizes of string is not known but obviously a 2 GB file has a lot of characters in it. This is because 1 character = 1 byte. So, about 1000 characters equal 1 kilobyte. If a page has 2000 characters on it (a reasonable average for a double-spaced page), then it will take up 2K (2 kilobytes). That means it will take about 500 pages of text to equal one megabyte. Hence 2 gigabyte = 2000 megabyte = 2000*500 = 10,00,000 pages of text !

From the above graph it is clear that the Naive Method (discussed in Set 1) performs the best (better than Segment Tree Method).

This is surprising as the asymptotic time complexity of Segment Tree Method is much lesser than that of the Naive Method.

In fact, the naive method is generally 5-6 times faster than the Segment Tree Method on typical files having random strings. Also not to forget that the Naive Method is an in-place algorithm, thus making it the most desirable algorithm to compute LCE .

The bottom-line is that the naive method is the most optimal choice for answering the LCE queries when it comes to average-case performance.

Such kind of thinks rarely happens in Computer Science when a faster-looking algorithm gets beaten by a less efficient one in practical tests.

What we learn is that the although asymptotic analysis is one of the most effective way to compare two algorithms on paper, but in practical uses sometimes things may happen the other way round.


This article is contributed by Rachit Belwariar. If you like GeeksforGeeks and would like to contribute, you can also write an article using or mail your article to See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

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Last Updated : 23 Dec, 2022
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