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Inorder traversal is a process for visiting each node in a binary tree by first visiting the left subtree, then the node itself, and then the right subtree. Unlike an array or a linked list, where elements can be visited in only one particular order, a non-linear data structure such as a tree can be traversed in many ways. With inorder traversal, the path always favors the leftmost tree before traversing the rest - in a binary search tree, this results in visiting the nodes in ascending order. As a way to retrieve data in a consistent order, inorder traversal is among the most common methods of tree traversal, alongside pre-order, post-order, and level-order traversal.

Software engineers apply tree traversal to perform operations on a tree in a specific order. For instance, when you need to find, add, or delete a certain value from a tree, we use a traversal approach to ensure that each node is visited in a specific order. Inorder traversal is particularly useful in binary search trees for getting values in ascending order, since by visiting the left subtree first, the left (smaller) node is always visited before the right (larger) node. Additionally, inorder traversal can be used to convert a binary search tree into a sorted array.

When should I use inorder traversal vs. preorder traversal vs. postorder traversal? Let’s talk about the specific approaches you can take below:

Inorder traversal visits each node in the tree by first traversing the left subtree, then visiting the node itself, and finally traversing the right subtree. Preorder traversal visits each node in the tree by first visiting the node itself, then traversing the left subtree, and finally traversing the right subtree. Inorder traversal has the benefit of being able to traverse a binary search tree in a ascending orde, because the node in the left subtree is always smaller than the node being visited, and the node in the right subtree is always larger.

Postorder traversal is a type of tree traversal where the nodes are visited in the order of left child, right child, root. This type of traversal is often used to delete the nodes of a tree in a specific order, because we can easily reconstruct the node references. Although inorder traversal is usually the better choice when you need to create a sorted list of values from a tree, postorder traversal is usually best applied to delete the nodes of a tree in a specific order.

Not exactly - inorder traversal is an order in which DFS (Depth-First Search) can be performed. DFS is a type of search algorithm that traverses a graph or tree data structure by always exploring deeper paths from the starting node until a leaf node is finally visited. But there are many paths that DFS can take, given that each node in a tree can have up to two children. Using DFS with inorder traversal results in visiting the left subtree first, the root, then the right subtree.

The time complexity of inorder traversal in a binary tree is O(n), where n is the number of nodes in the tree. This is because inorder traversal requires visiting each node in the tree exactly once, thus the time complexity is linear.

Iterative approaches to inorder traversal involve using a loop and a stack to traverse a tree. Although a tree is an inherently recursive data structure, this does not mean that one cannot also use iteration to traverse. Using a while loop to iterate, a stack can be used to keep track of the nodes that still need to be visited, ensuring that the most recently added nodes, the closest parents, are visited first. This approach allows for efficient traversal of the tree and requires less memory than recursive approaches.

Recursive approaches to inorder traversal involve using a recursive function to traverse a tree. This is often how DFS (Depth-First Search) is implemented, with a recursive function that visits each node in the tree by calling itself on each child node in a particular order. Using a recursive function, the algorithm keeps track of the nodes that need to be visited with the use of the call stack. This approach allows for efficient traversal of the tree, however it requires more memory than iterative approaches. Additionally, the recursive function must be carefully written to avoid infinite recursion.

```
class Node:
def __init__(self, data):
self.data = data
self.left = None
self.right = None
def inorder_traversal(node):
# base case: if node is null, do nothing
if node:
# recurse on left child
inorder_traversal(node.left)
# visit the current node
print(node.data)
# recurse on the right child
inorder_traversal(node.right)
```

```
1class Node:
2 def __init__(self, data):
3 self.data = data
4 self.left = None
5 self.right = None
6
7def inorder_traversal(node):
8 # base case: if node is null, do nothing
9 if node:
10 # recurse on left child
11 inorder_traversal(node.left)
12
13 # visit the current node
14 print(node.data)
15
16 # recurse on the right child
17 inorder_traversal(node.right)
```

Finally, let’s talk about practical ways you can use this approach as a software engineer.

Inorder traversal is a valuable technique to understand for technical interviews as it can help solve tree problems where nodes need to be visited in ascending order. By observing the candidate's approach to traversing the data structure, the interviewer can gain insight into the candidate's technical abilities.

Inorder traversal is used by software engineers when needing to traverse data stored in binary trees.This is especially useful when dealing with large datasets that are already organized in a binary search tree - inorder traversal can be used to create an ordered list of elements from the data, which can be useful for sorting, searching, and other operations.

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3/4

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Two elements of a binary search tree (BST) are swapped by mistake. Recover the tree without changing its structure.

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