Let us consider the following problem to understand Segment Trees without recursion.
We have an array arr[0 . . . n-1]. We should be able to,
- Find the sum of elements from index l to r where 0 <= l <= r <= n-1
- Change value of a specified element of the array to a new value x. We need to do 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. We have discussed the complete implementation of segment trees in our previous post. In this post we will discuss about a more easy and yet efficient implementation of segment trees than the previous post.
Consider the array and segment tree as shown below :
You can see from the above image that the original array is at the bottom and is 0-indexed with 16 elements. The tree contains a total of 31 nodes where the leaf nodes or the elements of original array starts from node 16. So, we can easily construct a segment tree for this array using a 2*N sized array where N is number of elements in original array. The leaf nodes will start from index N in this array and will go upto index (2*N – 1). Therefore and element at index i in original array will be at index (i + N) in the segment tree array. Now to calculate the parents, we will start from index (N – 1) and move upward. For an index i , its left child will be at (2 * i) and right child will be at (2*i + 1) index. So the values at nodes at (2 * i) and (2*i + 1) is combined at ith node to construct the tree.
As you can see in the above figure, we can query in this tree in an interval [L,R) with left index(L) included and right (R) excluded.
We will implement all of these multiplication and addition operations using bitwise operators.
Let us know have a look at the complete implementation:
Yes! This is all. Complete implementation of segment tree including the query and update functions in such a less number of lines of code than the previous recursive one. Let us now understand about how each of the function is working:
- The picture makes it clear that the leaf nodes are stored at i+n, so we can clearly insert all leaf nodes directly.
- The next step is to build the tree and it takes O(n) time. The parent has always it’s index less then its children so we just process all the nodes in decreasing order calculating the value of parent node. If the code inside the build function to calculate parents seems confusing then you can see this code, it is equivalent to that inside the build function.
- Updating a value at any position is also simple and the time taken will be proportional to the height of the tree. We only update values in the parents of the given node which is being changed. so for getting the parent , we just go up to the parent node , which is p/2 or p>>1, for node p. p^1 turns (2*i) to (2*i + 1) and vice versa to get the second child of p.
- Computing the sum also works in O(log(n)) time .if we work through an interval of [3,11), we need to calculate only for nodes 19,26,12 and 5 in that order.
The idea behind the query function is that whether we should include an element in the sum or we should include its parent. Let’s look at the image once again for proper understanding. Consider that L is the left border of an interval and R is the right border of the interval [L,R). It is clear from the image that if L is odd then it means that it is the right child of it’s parent and our interval includes only L and not it’s parent. So we will simply include this node to sum and move to the parent of it’s next node by doing L = (L+1)/2. Now, if L is even then it is the left child of it’s parent and interval includes it’s parent also unless the right borders interferes. Similar conditions is applied to the right border also for faster computation. We will stop this iteration once the left and right borders meet.
The theoretical time complexities of both previous implementation and this implementation is same but practically this is found to be much more efficient as there are no recursive calls. We simply iterate over the elements that we need. Also this is very easy to implement.
- Tree Construction : O( n )
- Query in Range : O( Log n )
- Updating an element : O( Log n ).
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- Overview of Data Structures | Set 3 (Graph, Trie, Segment Tree and Suffix Tree)
- Cartesian tree from inorder traversal | Segment Tree
- Build a segment tree for N-ary rooted tree
- Segment Tree | Set 1 (Sum of given range)
- Segment Tree | Set 2 (Range Minimum Query)
- Lazy Propagation in Segment Tree
- Longest Common Extension / LCE | Set 3 (Segment Tree Method)
- Reconstructing Segment Tree
- Segment Tree | Set 3 (XOR of given range)
- LIS using Segment Tree
- Segment Tree | (XOR of a given range )
- Levelwise Alternating OR and XOR operations in Segment Tree
- Euler Tour | Subtree Sum using Segment Tree
- Two Dimensional Segment Tree | Sub-Matrix Sum
- Segment Tree | Set 2 (Range Maximum Query with Node Update)
- Levelwise Alternating GCD and LCM of nodes in Segment Tree
- Iterative Segment Tree (Range Maximum Query with Node Update)
- Iterative Segment Tree (Range Minimum Query)
- Counting inversions in an array using segment tree
- Lazy Propagation in Segment Tree | Set 2