There are many problems in which you need to query on intervals or segments of a collection of items. For example, finding the sum of all the elements in an array from indexleft to right, or finding the minimum of all the elements in an array from indexleft to right. These problems can be easily solved with one of the most powerful data structures,Segment Tree( in-shortSegtree).What is Segment Tree ?Segment tree or segtree is a basically a binary tree used for storing the intervals or segments. Each node in the segment tree represents an interval.Consider an arrayAof sizeNand a corresponding segtreeT:The root ofTwill represent the whole arrayA[0:N-1].Each leaf in the segtreeTwill represent a single elementA[i]such

that0 <= i < N.The internal nodes in the segtree treeTrepresent union of

elementary intervalsA[i:j]where0 <= i < j < N.The root of the segtree will represent the whole arrayA[0:N-1]. Then we will break the interval or segment into half and the two children of the root will represent theA[0:(N-1) / 2]andA[(N-1) / 2 + 1:(N-1)]. So in each step we will divide the interval into half and the two children will represent the two halves. So the height of the segment tree will belog2N. There areNleaves representing theNelements of the array. The number of internal nodes isN-1. So total number of nodes are2*N – 1.Once we have built a segtree we cannot change its structure i.e., its structure is static. We can update the values of nodes but we cannot change its structure. Segment tree is recursive in nature. Because of its recursive nature, Segment tree is very easy to implement. Segment tree provides two operations:Update:In this operation we can update an element in the Array and

reflect the corresponding change in the Segment tree.Query:In this operation we can query on an interval or segment and

return the answer to the problem on that particular interval.Implementation:Since a segtree is abinary tree, we can use a simple linear array to represent the segment tree.

In almost any segtree problem we need think aboutwhat we need to store in the segment tree?.For example, if we want to find the sum of all the elements in an array from indexleft to right, then at each node (except leaf nodes) we will store the sum of its children nodes. If we want to find the minimum of all the elements in an array from indexleft to right, then at each node (except leaf nodes) we will store the minimum of its children nodes.Once we know what we need to store in the segment tree we can build the tree usingrecursion ( bottom-up approach ). We will start with the leaves and go up to the root and update the corresponding changes in the nodes that are in the path from leaves to root. Leaves represent a single element. In each step we will merge two children to form an internal node. Each internal node will represent a union of its children’s intervals. Merging may be different for different problems. So, recursion will end up at root which will represent the whole array.For update, we simply have to search the leaf that contains the element to update. This can be done by going to either on the left child or the right child depending on the interval which contains the element. Once we found the leaf, we will update it and again use the bottom-up approach to update the corresponding change in the path from leaf to root.To make a query on the segment tree we will be given a range froml to r. We will recurse on the tree starting from the root and check if the interval represented by the node is completely in the range froml to r. If the interval represented by a node is completely in the range froml to r, we will return that node’s value.

The segtree of arrayAof size7will look like :All this we will get clear by taking an example. You can given an arrayAof sizeNand some queries. There are two types of queries:Update:add a value of an element tovalin the arrayAat a

particular positionidx.Query:return the value ofA[l] + A[l+1] + A[l+2] + ….. + A[r-1] +

A[r]such that0 <= l <= r < NNaive Algorithm:This is the most basic approach. For query simple run a loop froml to rand calculate the sum of all the elements. So query will takeO(N).A[idx] += valwill update the value of the element. Update will takeO(1).

This algorithm is good if the number of update operations are very large and very few query operations.Another Naive Algorithm:In this approach we will preprocess and store the cumulative sum of the elements of the array in an arraysum. For each query simply return(sum[r] – sum[l-1]). Now query operation will takeO(1).

But to update, we need to run a loop and change the value of all thesum[i]such thatl <= i <= r. So update operation will takeO(N).

This algorithm is good if the number of query operations are very large and very few update operations.Using Segment Tree:Let us see how to use segment tree and what we will store in the segment tree in this problem. As we know that each node of the segtree will represent an interval or segment. In this problem we need to find the sum of all the elements in the given range. So in each node we will store the sum of all the elements of the interval represented by the node. How do we do that? We will build a segment tree using recursion ( bottom-up approach ) as explained above. Each leaf will have a single element. All the internal nodes will have the sum of both of its children.void build(int node, int start, int end)

{

if(start == end)

{

// Leaf node will have a single element

tree[node] = A[start];

}

else

{

int mid = (start + end) / 2;

// Recurse on the left child

build(2*node, start, mid);

// Recurse on the right child

build(2*node+1, mid+1, end);

// Internal node will have the sum of both of its children

tree[node] = tree[2*node] + tree[2*node+1];

}

}In the above code we will start from the root and recurse on the left and the right child until we reach the leaves. From the leaves we will go back to the root and update all the nodes in the path.noderepresent the current node we are processing. Since segment tree is a binary tree.2*nodewill represent the left node and2*node + 1represent the right node.startandendrepresents the interval represented by the node. Complexity of build() isO(N).To update an element we need to look at the interval in which the element is and recurse accordingly on the left or the right child.void update(int node, int start, int end, int idx, int val)

{

if(start == end)

{

// Leaf node

A[idx] += val;

tree[node] += val;

}

else

{

int mid = (start + end) / 2;

if(start <= idx and idx <= mid)

{

// If idx is in the left child, recurse on the left child

update(2*node, start, mid, idx, val);

}

else

{

// if idx is in the right child, recurse on the right child

update(2*node+1, mid+1, end, idx, val);

}

// Internal node will have the sum of both of its children

tree[node] = tree[2*node] + tree[2*node+1];

}

}Complexity of update will beO(logN).To query on a given range, we need to check 3 conditions.range represented by a node is completely inside the given rangerange represented by a node is completely outside the given rangerange represented by a node is partially inside and partially

outside the given rangeIf the range represented by a node is completely outside the given range, we will simply return 0. If the range represented by a node is completely inside the given range, we will return the value of the node which is the sum of all the elements in the range represented by the node. And if the range represented by a node is partially inside and partially outside the given range, we will return sum of the left child and the right child. Complexity of query will beO(logN).int query(int node, int start, int end, int l, int r)

{

if(r < start or end < l)

{

// range represented by a node is completely outside the given range

return 0;

}

if(l <= start and end <= r)

{

// range represented by a node is completely inside the given range

return tree[node];

}

// range represented by a node is partially inside and partially outside the given range

int mid = (start + end) / 2;

int p1 = query(2*node, start, mid, l, r);

int p2 = query(2*node+1, mid+1, end, l, r);

return (p1 + p2);

}Updating an interval ( Lazy Propagation ):Sometimes problems will ask you to update an interval froml to r, instead of a single element. One solution is to update all the elements one by one. Complexity of this approach will beO(N)per operation since where areNelements in the array and updating a single element will takeO(logN)time.To avoid multiple call to update function, we can modify the update function to work on an interval.void updateRange(int node, int start, int end, int l, int r, int val)

{

// out of range

if (start > end or start > r or end < l)

return;

// Current node is a leaf node

if (start == end)

{

// Add the difference to current node

tree[node] += val;

return;

}

// If not a leaf node, recur for children.

int mid = (start + end) / 2;

updateRange(node*2, start, mid, l, r, val);

updateRange(node*2 + 1, mid + 1, end, l, r, val);

// Use the result of children calls to update this node

tree[node] = tree[node*2] + tree[node*2+1];

}Let’s beLazyi.e., do work only when needed. How ? When we need to update an interval, we will update a node and mark its child that it needs to be updated and update it when needed. For this we need an arraylazy[]of the same size as that of segment tree. Initially all the elements of thelazy[]array will be0representing that there is no pending update. If there is non-zero elementlazy[k]then this element needs to update nodekin the segment tree before making any query operation.To update an interval we will keep 3 things in mind.If current segment tree node has any pending update, then first add

that pending update to current node.If the interval represented by current node lies completely in the

interval to update, then update the current node and update thelazy[]array for children nodes.If the interval represented by current node overlaps with the

interval to update, then update the nodes as the earlier update

functionSince we have changed the update function to postpone the update operation, we will have to change the query function also. The only change we need to make is to check if there is any pending update operation on that node. If there is a pending update operation, first update the node and then work same as the earlier query function.void updateRange(int node, int start, int end, int l, int r, int val)

{

if(lazy[node] != 0)

{

// This node needs to be updated

tree[node] += (end – start + 1) * lazy[node]; // Update it

if(start != end)

{

lazy[node*2] += lazy[node]; // Mark child as lazy

lazy[node*2+1] += lazy[node]; // Mark child as lazy

}

lazy[node] = 0; // Reset it

}

if(start > end or start > r or end < l) // Current segment is not within range [l, r]

return;

if(start >= l and end <= r)

{

// Segment is fully within range

tree[node] += (end – start + 1) * val;

if(start != end)

{

// Not leaf node

lazy[node*2] += val;

lazy[node*2+1] += val;

}

return;

}

int mid = (start + end) / 2;

updateRange(node*2, start, mid, l, r, val); // Updating left child

updateRange(node*2 + 1, mid + 1, end, l, r, val); // Updating right child

tree[node] = tree[node*2] + tree[node*2+1]; // Updating root with max value

}

int queryRange(int node, int start, int end, int l, int r)

{

if(start > end or start > r or end < l)

return 0; // Out of range

if(lazy[node] != 0)

{

// This node needs to be updated

tree[node] += (end – start + 1) * lazy[node]; // Update it

if(start != end)

{

lazy[node*2] += lazy[node]; // Mark child as lazy

lazy[node*2+1] += lazy[node]; // Mark child as lazy

}

lazy[node] = 0; // Reset it

}

if(start >= l and end <= r) // Current segment is totally within range [l, r]

return tree[node];

int mid = (start + end) / 2;

int p1 = queryRange(node*2, start, mid, l, r); // Query left child

int p2 = queryRange(node*2 + 1, mid + 1, end, l, r); // Query right child

return (p1 + p2);

}Practice Problems:Help AshuRoy And Coin BoxesComrades-IIISolve Problems