15.2. Finding the Maximum

Now, like binary search you can do iterative version of this, so you start at the root and then so long as it is not nil you trust on the path. So, the current values we return true; otherwise, you walked on to the left are you actually start to the root and you kind of trace a path you can values are that find.

This chunk introduces the process of finding the minimum and maximum values in a binary search tree (BST). It highlights the iterative and recursive methods used for this purpose, starting from the root node and exploring the left and right paths based on whether we are looking for the minimum or maximum value.

Imagine a library where the books are arranged in order of height. To find the shortest book (minimum), you would start from the first shelf and keep moving left until you can't go further. Similarly, to find the tallest book (maximum), you'd start from the first shelf and keep moving right.

So, the minimum node in the tree is the left most node, in other wards it is the node that you reach if you go and as for as you can follow only left edges.

In a binary search tree, the minimum value is found by continually moving left from the root until no further left child exists. This is because, in a BST, all left children contain values smaller than their parent node. Therefore, the leftmost node will hold the minimum value.

Think of a tree as a stack of boxes, where smaller boxes are always stacked on top of larger ones. To find the smallest box, you would keep removing the top boxes (moving left) until you can't go any higher—at which point you'll find the smallest box at the bottom.

So, we can easily find it recursively, now we will typically use this assuming the trees not empty, it simplifies a little bit of coding.

To implement the iterative approach for finding the minimum value, we use a loop that keeps following the left child of the current node until it reaches a node that has no left child (i.e., the left child is nil), at which point we have found the minimum value.

Consider searching for the lowest point in a valley by walking downhill. You keep going left (downhill) until you reach the lowest point (nil). This approach ensures you find the lowest spot without stubbornly reversing directions.

So, symmetrically the maximum is the right most value on the tree, as you go right you go bigger, if you cannot go right any more this is nothing which is bigger than that node.

Similarly, the maximum value in a binary search tree is located by continuously moving to the right child of the current node until a node has no right child. The rightmost node contains the maximum value because, in a BST, right children have values greater than their parent nodes.

Think of climbing up a ladder where each step represents a larger height. To find the highest point, you keep going up (to the right) until there are no more steps. This gives you the maximum height.

And again iterative version of the same, we just follow chase point as to the right. So, as long as right t dot right is not nil, we move from t to t dot right.

The iterative method for finding the maximum follows the same logic as finding the minimum but in the opposite direction. You keep moving to the right child of the current node until there are no more right children. At this point, we have reached the maximum node.

Picture a mountain trail where each step forward leads you higher. To find the peak, you keep walking straight ahead (to the right) until no more paths (right children) exist. Once you arrive at the highest point, you've found the maximum.

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