Segment Tree | Set 1 (Sum of given range)
Let us consider the following problem to understand Segment Trees.
We have an array arr[0 . . . n-1]. We should be able to
1 Find the sum of elements from index l to r where 0 <= l <= r <= n-1
2 Change value of a specied element of the array arr[i] = x where 0 <= i <= n-1.
A simple solution is to run a loop from l to r and calculate sum of elements in given range. To update a value, simply do arr[i] = x. The first operation takes O(n) time and second operation takes O(1) time.
Another solution is to create another array and store sum from start to i at the ith index in this array. Sum of a given range can now be calculated in O(1) time, but update operation takes O(n) time now. This works well if the number of query operations are large and very few updates.
What if the number of query and updates are equal? Can we perform both the operations in O(log n) time once given the array? We can use a Segment Tree to do both operations in O(Logn) time.
Representation of Segment trees
1. Leaf Nodes are the elements of the input array.
2. Each internal node represents some merging of the leaf nodes. The merging may be different for different problems. For this problem, merging is sum of leaves under a node.
An array representation of tree is used to represent Segment Trees. For each node at index i, the left child is at index 2*i+1, right child at 2*i+2 and the parent is at
.
Construction of Segment Tree from given array
We start with a segment arr[0 . . . n-1]. and every time we divide the current segment into two halves(if it has not yet become a segment of length 1), and then call the same procedure on both halves, and for each such segment we store the sum in corresponding node.
All levels of the constructed segment tree will be completely filled except the last level. Also, the tree will be a Full Binary Tree because we always divide segments in two halves at every level. Since the constructed tree is always full binary tree with n leaves, there will be n-1 internal nodes. So total number of nodes will be 2*n – 1.
Height of the segment tree will be
. Since the tree is represented using array and relation between parent and child indexes must be maintained, size of memory allocated for segment tree will be
.
Query for Sum of given range
Once the tree is constructed, how to get the sum using the constructed segment tree. Following is algorithm to get the sum of elements.
int getSum(node, l, r)
{
if range of node is within l and r
return value in node
else if range of node is completely outside l and r
return 0
else
return getSum(node's left child, l, r) +
getSum(node's right child, l, r)
}
Update a value
Like tree construction and query operations, update can also be done recursively. We are given an index which needs to updated. Let diff be the value to be added. We start from root of the segment tree, and add diff to all nodes which have given index in their range. If a node doesn’t have given index in its range, we don’t make any changes to that node.
Implementation:
Following is implementation of segment tree. The program implements construction of segment tree for any given array. It also implements query and update operations.
// Program to show segment tree operations like construction, query and update
#include <stdio.h>
#include <math.h>
// 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 sum of values in given range of the array.
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 getSumUtil(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
// sum 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 0;
// If a part of this segment overlaps with the given range
int mid = getMid(ss, se);
return getSumUtil(st, ss, mid, qs, qe, 2*index+1) +
getSumUtil(st, mid+1, se, qs, qe, 2*index+2);
}
/* A recursive function to update the nodes which have the given index in
their range. The following are parameters
st, index, ss and se are same as getSumUtil()
i --> index of the element to be updated. This index is in input array.
diff --> Value to be added to all nodes which have i in range */
void updateValueUtil(int *st, int ss, int se, int i, int diff, int index)
{
// Base Case: If the input index lies outside the range of this segment
if (i < ss || i > se)
return;
// If the input index is in range of this node, then update the value
// of the node and its children
st[index] = st[index] + diff;
if (se != ss)
{
int mid = getMid(ss, se);
updateValueUtil(st, ss, mid, i, diff, 2*index + 1);
updateValueUtil(st, mid+1, se, i, diff, 2*index + 2);
}
}
// The function to update a value in input array and segment tree.
// It uses updateValueUtil() to update the value in segment tree
void updateValue(int arr[], int *st, int n, int i, int new_val)
{
// Check for erroneous input index
if (i < 0 || i > n-1)
{
printf("Invalid Input");
return;
}
// Get the difference between new value and old value
int diff = new_val - arr[i];
// Update the value in array
arr[i] = new_val;
// Update the values of nodes in segment tree
updateValueUtil(st, 0, n-1, i, diff, 0);
}
// Return sum of elements in range from index qs (quey start) to
// qe (query end). It mainly uses getSumUtil()
int getSum(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 getSumUtil(st, 0, n-1, qs, qe, 0);
}
// A recursive function that constructs Segment Tree for array[ss..se].
// 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 sum of values in this node
int mid = getMid(ss, se);
st[si] = 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
int x = (int)(ceil(log2(n))); //Height of segment tree
int max_size = 2*(int)pow(2, x) - 1; //Maximum size of segment tree
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;
}
// Driver program to test above functions
int main()
{
int arr[] = {1, 3, 5, 7, 9, 11};
int n = sizeof(arr)/sizeof(arr[0]);
// Build segment tree from given array
int *st = constructST(arr, n);
// Print sum of values in array from index 1 to 3
printf("Sum of values in given range = %d\n", getSum(st, n, 1, 3));
// Update: set arr[1] = 10 and update corresponding segment
// tree nodes
updateValue(arr, st, n, 1, 10);
// Find sum after the value is updated
printf("Updated sum of values in given range = %d\n",
getSum(st, n, 1, 3));
return 0;
}
Output:
Sum of values in given range = 15 Updated sum of values in given range = 22
Time Complexity:
Time Complexity for tree construction is O(n). There are total 2n-1 nodes, and value of every node is calculated only once in tree construction.
Time complexity to query is O(Logn). To query a sum, we process at most two nodes at every level and number of levels is O(Logn).
The time complexity of update is also O(Logn). To update a leaf value, we process one node at every level and number of levels is O(Logn).
References:
http://www.cse.iitk.ac.in/users/aca/lop12/slides/06.pdf
Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

How do we do the updation if we have to update more than 2 values
like we have to increase all number in range a to b by 2
how our update function do this in O(log(n))
can any body plz help
Since the tree is represented using array and relation between parent and child indexes must be maintained, size of memory allocated for segment tree will be 2*(2^ceil(log2n)) - 1.
Why not just 2*n - 1 ? What are the bad sequences of just allotting 2*n - 1 nodes to the tree. Where will it go wrong?
Anybody pls help..
The array must have enough elements to include a possible right-most leaf. The index of a possible right-most leaf increases with a step of power of 2. The size of (2*n-1) might be just not big enough.
/ Alexander K.
Thanks.. that made it clear..
Intelligent
I have difficulty understanding the time complexity of FindMin().
At each node we are splitting the problem in to two sub problems of equal size.
T(n) = 2T(n/2) + 1;
I think this reduces to O(n).
Please correct me if I am wrong any where?
I think in updateValueUti function it should be
updateValueUtil(st, ss, mid, i, diff, 2*index + 1) instead of
updateValueUtil(st, 0, mid, i, diff, 2*index + 1)
Thanks for pointing this out. We have updated the code.
This is incorrect...
Let us reconsider the example of array 1,3,5,7,9,11
if i have to calculate sum of indices 2 to 4. This should be 5+7+9=21. But using segment tree it is not possible to calculate the same.
Segment tree will work only if the indices are in first half or 2nd half, but when the indices span across halves it might not work
We ran the above given code for your input and it produced the correct output. Did you run the code?
BIT is more efficient in this case. Relatively faster than Segment trees, Lesser memory requirements:
Time complexities : O(log N)
Space complexities: O(N)
More details here: http://www.algorithmist.com/index.php/Fenwick_tree
#include <vector>
using namespace std;
// In this implementation, the tree is represented by a vector<int>.
// Elements are numbered by 0, 1, ..., n-1.
// tree[i] is sum of elements with indexes i&(i+1)..i, inclusive.
// Creates a zero-initialized Fenwick tree for n elements.
vector<int> create(int n) { return vector<int>(n, 0); }
// Returns sum of elements with indexes a..b, inclusive
int query(const vector<int> &tree, int a, int b) {
if (a == 0) {
int sum = 0;
for (; b >= 0; b = (b & (b + 1)) - 1)
sum += tree[b];
return sum;
} else {
return query(tree, 0, b) - query(tree, 0, a-1);
}
}
// Increases value of k-th element by inc.
void increase(vector<int> &tree, int k, int inc) {
for (; k < (int)tree.size(); k |= k + 1)
tree[k] += inc;
}
Nice explanation. But there's another tree structure which is precisely meant to answer range-sum & product queries called Binary indexed trees, which is simpler, powerful and easy to maintain.
Segment trees are really good for answering range minimum queries & intervals.
Here's a complete working implementation of segment tree in C#
/* Paste your code here (You may delete these lines if not writing code)
using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using Algorithms.Trees.Base;
namespace Algorithms.Trees
{
public class SegmentTree<K> where K:IComparable<K>
{
private List<SegmentTreeNode<K>> Elements;
public K[] Keys { get; set; }
public SegmentTree(K[] keys)
{
if (null == keys || 0 == keys.Length)
{
return;
}
Keys = keys;
int segmentTreeSize = (int)Math.Pow(2, Math.Log(Keys.Length, 2) + 1);
Elements = new List<SegmentTreeNode<K>>(segmentTreeSize);
for (int i = 0; i < segmentTreeSize; i++)
{
Elements.Add(null);
}
ConstructSegmentTreeForRange(0, keys.Length - 1, 0);
}
SegmentTreeNode<K> ConstructSegmentTreeForRange(int lowIndex, int highIndex, int rangeIndex)
{
SegmentTreeNode<K> rangeNode = new SegmentTreeNode<K>() { LowIndex = lowIndex, HighIndex = highIndex };
K leftMinimum, rightMinimum;
int midPoint = lowIndex + (highIndex - lowIndex)/2;
if (lowIndex < highIndex)
{
leftMinimum = ConstructSegmentTreeForRange(lowIndex, midPoint, 2 * rangeIndex + 1).RangeMinimum;
rightMinimum = ConstructSegmentTreeForRange(midPoint + 1, highIndex, 2 * rangeIndex + 2).RangeMinimum;
rangeNode.RangeMinimum = (leftMinimum.CompareTo(rightMinimum) <= 0) ? leftMinimum : rightMinimum;
}
else if (lowIndex == highIndex)
{
rangeNode.RangeMinimum = Keys[lowIndex];
rangeNode.LowIndex = lowIndex;
rangeNode.HighIndex = lowIndex;
}
Elements[rangeIndex] = rangeNode;
return rangeNode;
}
public K QueryMinimumInRange(int lowIndex, int highIndex)
{
if (lowIndex < 0 || highIndex >= Keys.Length)
{
return default(K);
}
return QueryMinimumInRangeAux(lowIndex, highIndex, 0);
}
private K QueryMinimumInRangeAux(int lowIndex, int highIndex, int rangeIndex)
{
if (highIndex < Elements[rangeIndex].LowIndex || lowIndex > Elements[rangeIndex].HighIndex)
{
return default(K);
}
if (Elements[rangeIndex].LowIndex >= lowIndex && Elements[rangeIndex].HighIndex <= highIndex)
{
return Elements[rangeIndex].RangeMinimum;
}
K leftMinimum = QueryMinimumInRangeAux(lowIndex, highIndex, 2 * rangeIndex + 1);
K rightMinimum = QueryMinimumInRangeAux(lowIndex, highIndex, 2 * rangeIndex + 2);
if (EqualityComparer<K>.Default.Equals(default(K), leftMinimum))
{
return rightMinimum;
}
if (EqualityComparer<K>.Default.Equals(default(K), rightMinimum))
{
return leftMinimum;
}
return (leftMinimum.CompareTo(rightMinimum) <= 0) ? leftMinimum : rightMinimum;
}
public bool UpdateKey(int keyIndex, K newKey)
{
if (keyIndex < 0 || keyIndex >= Keys.Length)
{
return false;
}
Keys[keyIndex] = newKey;
return UpdateKeyAux(keyIndex, newKey, 0);
}
private bool UpdateKeyAux(int keyIndex, K newKey, int rangeIndex)
{
if (Elements[rangeIndex].LowIndex == keyIndex && Elements[rangeIndex].HighIndex == keyIndex)
{
Elements[rangeIndex].RangeMinimum = newKey;
return true;
}
if (Elements[rangeIndex].RangeMinimum.CompareTo(newKey) > 0)
{
Elements[rangeIndex].RangeMinimum = newKey;
}
int midPoint = Elements[rangeIndex].LowIndex + (Elements[rangeIndex].HighIndex - Elements[rangeIndex].LowIndex) / 2;
if (keyIndex <= midPoint)
{
return UpdateKeyAux(keyIndex, newKey, 2 * rangeIndex + 1);
}
else
{
return UpdateKeyAux(keyIndex, newKey, 2 * rangeIndex + 2);
}
}
}
}
*/
Hi, I think this is incorrect. constructSTUtil(arr, 0, n-1, st, 0); // Return the constructed segment tree return st; This should do the job. st = constructSTUtil(arr, 0, n-1, st, 0); Please correct me else. ThanksPlease take a closer look at the code. The recursive function constructSTUtil() returns the value of root (or sum of leaf nodes under it). st is a pointer to the constructed segment tree.
Ok, yes. My bad!
May you please give the code to implement segment tree to store intervals ?
Yeah ..can you please provide that implementation also ?
You are using 0th location also, so, left child is at 2*i+1 and right child at 2*i+2. If i is either left or right child location, parent is at i/2.
Power function can be excluded with simple shift operation.
@Venki: Thanks for pointing this out. The line of explanation was for starting index 1 and code for starting index 0. We have updated the explanation to match with code. The code and other explanation remains same.