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Binary Tree Pruning

#1019 Most AskedMedium

Binary Tree Pruning #1019 Most Asked

Medium

Microsoft

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Topics:

TreesRecursion

Given the root of a binary tree, return the same tree where every subtree (of the given tree) not containing a 1 has been removed.

A subtree of a node node is node plus every node that is a descendant of node.

Example 1:

Input: root = [1,null,0,0,1]
Output: [1,null,0,null,1]
Explanation: 
Only the red nodes satisfy the property "every subtree not containing a 1".
The diagram on the right represents the answer.

Example 2:

Input: root = [1,0,1,0,0,0,1]
Output: [1,null,1,null,1]

Example 3:

Input: root = [1,1,0,1,1,0,1,0]
Output: [1,1,0,1,1,null,1]

Constraints:

The number of nodes in the tree is in the range [1, 200].
Node.val is either 0 or 1.

Try coding it in LeetCode

Binary Tree Pruning

#1019 Most AskedMedium

Binary Tree Pruning #1019 Most Asked

Medium

Microsoft

3 views

Topics:

TreesRecursion

Given the root of a binary tree, return the same tree where every subtree (of the given tree) not containing a 1 has been removed.

A subtree of a node node is node plus every node that is a descendant of node.

Example 1:

Input: root = [1,null,0,0,1]
Output: [1,null,0,null,1]
Explanation: 
Only the red nodes satisfy the property "every subtree not containing a 1".
The diagram on the right represents the answer.

Example 2:

Input: root = [1,0,1,0,0,0,1]
Output: [1,null,1,null,1]

Example 3:

Input: root = [1,1,0,1,1,0,1,0]
Output: [1,1,0,1,1,null,1]

Constraints:

The number of nodes in the tree is in the range [1, 200].
Node.val is either 0 or 1.

Try coding it in LeetCode

Solution

Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

What is the range of values for the nodes in the binary tree? Are negative values possible?
What should I return if the root is null or if the tree becomes entirely empty after pruning?
If a node has value '0' but one of its subtrees needs to be kept, should that node be kept as well?
Can I modify the original tree structure directly, or should I create and return a new pruned tree?
Are the node values integers, or could they be floating-point numbers?

Brute Force Solution

Approach

The goal is to remove parts of a tree that are all zeros. A brute force approach checks every single part of the tree to see if it can be removed. It explores all possible combinations of removing or keeping each part.

Here's how the algorithm would work step-by-step:

Start by looking at the very top of the tree.
Decide whether to keep it or remove it.
If you keep it, look at the next level down on the left side, and decide whether to keep that or remove it.
Do the same thing for the right side of the top.
Keep going down the tree, level by level, making a choice at each part whether to keep it or remove it.
After you've reached the bottom and made all your keep/remove choices, check if the whole tree is now just zeros.
If it is, this is a possible solution.
Do this over and over again, trying EVERY possible combination of keeping and removing parts.
Once you've tried every single combination, choose the one that removes as many unnecessary zeros as possible while still following the rules.

Code Implementation

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def pruneTreeBruteForce(root):

    def is_all_zeros(node):
        if not node:
            return True
        if node.val != 0:
            return False
        return is_all_zeros(node.left) and is_all_zeros(node.right)

    def all_possible_trees(node):
        if not node:
            return [None]

        left_trees = all_possible_trees(node.left)
        right_trees = all_possible_trees(node.right)

        possible_trees = []
        for left_tree in left_trees:
            for right_tree in right_trees:
                # Keep current node, explore its children.
                new_node = TreeNode(node.val, left_tree, right_tree)
                possible_trees.append(new_node)

                # Prune left.
                new_node = TreeNode(node.val, None, right_tree)
                possible_trees.append(new_node)

                # Prune right.
                new_node = TreeNode(node.val, left_tree, None)
                possible_trees.append(new_node)

                # Prune both children.
                new_node = TreeNode(node.val, None, None)
                possible_trees.append(new_node)

        return possible_trees

    possible_trees = all_possible_trees(root)

    best_tree = None
    max_nodes_removed = -1

    for possible_tree in possible_trees:
        if possible_tree is None:
            if max_nodes_removed < 0:
                max_nodes_removed = 0
                best_tree = None
            continue

        if is_all_zeros(possible_tree):
            nodes_removed = count_nodes(root) if root else 0
        else:
            nodes_removed = count_nodes(root) - count_nodes(possible_tree)

        if nodes_removed > max_nodes_removed:
            # Found a better pruned tree.
            max_nodes_removed = nodes_removed
            best_tree = possible_tree

    return best_tree

def count_nodes(root):
    if not root:
        return 0
    return 1 + count_nodes(root.left) + count_nodes(root.right)

Big(O) Analysis

Time Complexity

O(2^n) – The described brute force approach explores every possible combination of keeping or removing each node in the binary tree. For a tree with n nodes, each node has two choices: keep or remove. Therefore, there are 2^n possible combinations of tree structures to evaluate. Checking if each of these 2^n resulting trees consists entirely of zeros requires traversing the tree, which takes O(n) time in the worst case. Thus, the overall time complexity is O(n * 2^n); however, since 2^n dominates n, the complexity is approximated to O(2^n).

Space Complexity

O(N) – The brute force approach explores every possible combination of keeping or removing nodes in the binary tree. Since the algorithm uses recursion to traverse the tree, the maximum depth of the recursion stack can be equal to the number of nodes, N, in the worst-case scenario (e.g., a skewed tree). Each recursive call requires a stack frame to store local variables and return addresses. Therefore, the space complexity is O(N) due to the recursion depth.

Optimal Solution

Approach

The goal is to remove parts of a tree that are not important. We'll go through the tree from the bottom up, deciding at each point whether to keep it or cut it off based on what's below.

Here's how the algorithm would work step-by-step:

Start at the very bottom of the tree, at the leaves.
Look at each leaf: If it's a zero, we can get rid of it.
Move up to the nodes just above the leaves. For each node, check its children. If both children are gone (because they were zero or already cut off), and the node itself is a zero, then get rid of the node too.
Keep going up the tree, level by level. At each node, check if its children are gone and if the node itself is zero. If so, remove it.
Repeat this process until you reach the very top of the tree. The remaining tree is the pruned version.

Code Implementation

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def pruneTree(root):
    # type root: TreeNode
    # type: -> TreeNode
    def prune_subtree(node):
        if not node:
            return None

        node.left = prune_subtree(node.left)
        node.right = prune_subtree(node.right)

        # If node is zero and has no children, prune it.
        if node.val == 0 and not node.left and not node.right:
            return None

        # We need to keep the node in other cases.
        return node

    # Kick off the recursive pruning process
    return prune_subtree(root)

Big(O) Analysis

Time Complexity

O(n) – The algorithm performs a traversal of the binary tree, visiting each node once. At each node, it performs a constant amount of work, specifically checking the values of its children and the node itself to determine if pruning is necessary. Since the number of nodes in the tree is proportional to the input size 'n', the total time complexity is directly proportional to 'n'.

Space Complexity

O(H) – The algorithm's space complexity is primarily determined by the recursion depth, where H is the height of the binary tree. In the worst-case scenario, the recursion stack can grow up to the height of the tree if the tree is skewed (e.g., a linked list). Therefore, the auxiliary space used is proportional to the height H of the tree, representing the maximum number of function calls on the call stack at any given time. This leads to a space complexity of O(H), where H is the height of the tree, and in the worst case (skewed tree), H can be N (number of nodes).

Edge Cases

Case	How to Handle
Null or Empty Tree	Return null immediately as there is nothing to prune.
Tree with a single node (root)	Check the root node's val; if it's 0, return null; otherwise, return the root.
Tree with all nodes having value 0	The pruning should result in a null tree and the algorithm should correctly prune all nodes.
Tree with all nodes having value 1	The tree remains unchanged after pruning as no node is pruned.
Skewed tree (e.g., all nodes on the left)	The recursive calls should handle the skewed nature without stack overflow (within reasonable tree depth limits), correctly pruning 0-valued branches.
Deeply nested tree leading to stack overflow (language dependent)	Consider an iterative approach or tail-call optimization (if the language supports it) to prevent stack overflow for extremely deep trees.
Tree with a large number of nodes (memory constraints)	Ensure the pruning algorithm is efficient in terms of memory usage to avoid out-of-memory errors.
Subtree with a non-zero value near a zero-valued root	The recursive post-order traversal ensures that the subtree is visited before the root, preserving it.

Null or Empty Tree

How to Handle:

Return null immediately as there is nothing to prune.

Tree with a single node (root)

How to Handle:

Check the root node's val; if it's 0, return null; otherwise, return the root.

Tree with all nodes having value 0

How to Handle:

The pruning should result in a null tree and the algorithm should correctly prune all nodes.

Tree with all nodes having value 1

How to Handle:

The tree remains unchanged after pruning as no node is pruned.

Skewed tree (e.g., all nodes on the left)

How to Handle:

The recursive calls should handle the skewed nature without stack overflow (within reasonable tree depth limits), correctly pruning 0-valued branches.

Deeply nested tree leading to stack overflow (language dependent)

How to Handle:

Consider an iterative approach or tail-call optimization (if the language supports it) to prevent stack overflow for extremely deep trees.

Tree with a large number of nodes (memory constraints)

How to Handle:

Ensure the pruning algorithm is efficient in terms of memory usage to avoid out-of-memory errors.

Subtree with a non-zero value near a zero-valued root

How to Handle:

The recursive post-order traversal ensures that the subtree is visited before the root, preserving it.

Solution

Clarifying Questions

When you get asked this question in a real-life environment, it will often be ambiguous (especially at FAANG). Make sure to ask these questions in that case:

What is the range of values for the nodes in the binary tree? Are negative values possible?
What should I return if the root is null or if the tree becomes entirely empty after pruning?
If a node has value '0' but one of its subtrees needs to be kept, should that node be kept as well?
Can I modify the original tree structure directly, or should I create and return a new pruned tree?
Are the node values integers, or could they be floating-point numbers?

Brute Force Solution

Approach

Here's how the algorithm would work step-by-step:

Start by looking at the very top of the tree.
Decide whether to keep it or remove it.
If you keep it, look at the next level down on the left side, and decide whether to keep that or remove it.
Do the same thing for the right side of the top.
Keep going down the tree, level by level, making a choice at each part whether to keep it or remove it.
After you've reached the bottom and made all your keep/remove choices, check if the whole tree is now just zeros.
If it is, this is a possible solution.
Do this over and over again, trying EVERY possible combination of keeping and removing parts.
Once you've tried every single combination, choose the one that removes as many unnecessary zeros as possible while still following the rules.

Code Implementation

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def pruneTreeBruteForce(root):

    def is_all_zeros(node):
        if not node:
            return True
        if node.val != 0:
            return False
        return is_all_zeros(node.left) and is_all_zeros(node.right)

    def all_possible_trees(node):
        if not node:
            return [None]

        left_trees = all_possible_trees(node.left)
        right_trees = all_possible_trees(node.right)

        possible_trees = []
        for left_tree in left_trees:
            for right_tree in right_trees:
                # Keep current node, explore its children.
                new_node = TreeNode(node.val, left_tree, right_tree)
                possible_trees.append(new_node)

                # Prune left.
                new_node = TreeNode(node.val, None, right_tree)
                possible_trees.append(new_node)

                # Prune right.
                new_node = TreeNode(node.val, left_tree, None)
                possible_trees.append(new_node)

                # Prune both children.
                new_node = TreeNode(node.val, None, None)
                possible_trees.append(new_node)

        return possible_trees

    possible_trees = all_possible_trees(root)

    best_tree = None
    max_nodes_removed = -1

    for possible_tree in possible_trees:
        if possible_tree is None:
            if max_nodes_removed < 0:
                max_nodes_removed = 0
                best_tree = None
            continue

        if is_all_zeros(possible_tree):
            nodes_removed = count_nodes(root) if root else 0
        else:
            nodes_removed = count_nodes(root) - count_nodes(possible_tree)

        if nodes_removed > max_nodes_removed:
            # Found a better pruned tree.
            max_nodes_removed = nodes_removed
            best_tree = possible_tree

    return best_tree

def count_nodes(root):
    if not root:
        return 0
    return 1 + count_nodes(root.left) + count_nodes(root.right)

Big(O) Analysis

Time Complexity

Space Complexity

Optimal Solution

Approach

The goal is to remove parts of a tree that are not important. We'll go through the tree from the bottom up, deciding at each point whether to keep it or cut it off based on what's below.

Here's how the algorithm would work step-by-step:

Start at the very bottom of the tree, at the leaves.
Look at each leaf: If it's a zero, we can get rid of it.
Move up to the nodes just above the leaves. For each node, check its children. If both children are gone (because they were zero or already cut off), and the node itself is a zero, then get rid of the node too.
Keep going up the tree, level by level. At each node, check if its children are gone and if the node itself is zero. If so, remove it.
Repeat this process until you reach the very top of the tree. The remaining tree is the pruned version.

Code Implementation

class TreeNode:
    def __init__(self, val=0, left=None, right=None):
        self.val = val
        self.left = left
        self.right = right

def pruneTree(root):
    # type root: TreeNode
    # type: -> TreeNode
    def prune_subtree(node):
        if not node:
            return None

        node.left = prune_subtree(node.left)
        node.right = prune_subtree(node.right)

        # If node is zero and has no children, prune it.
        if node.val == 0 and not node.left and not node.right:
            return None

        # We need to keep the node in other cases.
        return node

    # Kick off the recursive pruning process
    return prune_subtree(root)

Big(O) Analysis

Time Complexity

Space Complexity

Edge Cases

Case	How to Handle
Null or Empty Tree	Return null immediately as there is nothing to prune.
Tree with a single node (root)	Check the root node's val; if it's 0, return null; otherwise, return the root.
Tree with all nodes having value 0	The pruning should result in a null tree and the algorithm should correctly prune all nodes.
Tree with all nodes having value 1	The tree remains unchanged after pruning as no node is pruned.
Skewed tree (e.g., all nodes on the left)	The recursive calls should handle the skewed nature without stack overflow (within reasonable tree depth limits), correctly pruning 0-valued branches.
Deeply nested tree leading to stack overflow (language dependent)	Consider an iterative approach or tail-call optimization (if the language supports it) to prevent stack overflow for extremely deep trees.
Tree with a large number of nodes (memory constraints)	Ensure the pruning algorithm is efficient in terms of memory usage to avoid out-of-memory errors.
Subtree with a non-zero value near a zero-valued root	The recursive post-order traversal ensures that the subtree is visited before the root, preserving it.

Null or Empty Tree

How to Handle:

Return null immediately as there is nothing to prune.

Tree with a single node (root)

How to Handle:

Check the root node's val; if it's 0, return null; otherwise, return the root.

Tree with all nodes having value 0

How to Handle:

The pruning should result in a null tree and the algorithm should correctly prune all nodes.

Tree with all nodes having value 1

How to Handle:

The tree remains unchanged after pruning as no node is pruned.

Skewed tree (e.g., all nodes on the left)

How to Handle:

The recursive calls should handle the skewed nature without stack overflow (within reasonable tree depth limits), correctly pruning 0-valued branches.

Deeply nested tree leading to stack overflow (language dependent)

How to Handle:

Consider an iterative approach or tail-call optimization (if the language supports it) to prevent stack overflow for extremely deep trees.

Tree with a large number of nodes (memory constraints)

How to Handle:

Ensure the pruning algorithm is efficient in terms of memory usage to avoid out-of-memory errors.

Subtree with a non-zero value near a zero-valued root

How to Handle:

The recursive post-order traversal ensures that the subtree is visited before the root, preserving it.

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