Problem Description

In this problem, we're given the root of a binary tree that represents a boolean expression. The leaves of this tree have values of either 0 or 1, which stand for the boolean values false and true, respectively. The non-leaf nodes can have one of four values: 2 (OR operation), 3 (AND operation), 4 (XOR operation), or 5 (NOT operation). The goal is to perform a minimum number of flips on the leaf nodes, where a flip changes a 0 to a 1 or a 1 to a 0, so that when the expression represented by the whole tree is evaluated, it yields the boolean value given by the variable result.

It's interesting to note that for the NOT operation, which is represented by the value 5, the tree node will have only one child. For all other operations, there will be two children.

The problem assures us that it is always possible to achieve the desired result through a series of flips. However, the challenge is to find the minimum number of flips needed to do so.

Flowchart Walkthrough

First, let's utilize the Flowchart for algorithm selection. Here's a thorough analysis to determine the appropriate approach for problem LeetCode 2313. Minimum Flips in Binary Tree to Get Result:

Is it a graph?

• Yes: A binary tree is a specialized form of a graph.

Is it a tree?

• Yes: The problem explicitly mentions that the structure in question is a binary tree.

DFS

Conclusion: Since a binary tree is a specific type of graph and the goal involves modifying or checking the tree to satisfy certain conditions, Depth-First Search (DFS) is a suitable algorithm. DFS will allow us to explore each node (representing tree elements) effectively, making it the apt choice for examining and manipulating individual nodes (flipping from 0 to 1 or vice versa) based on the required outcome.

Intuition

To solve the problem, we must carefully navigate the tree and consider the cost (in terms of flips) of getting to the desired result. We'll need to perform a depth-first traversal of the tree. For each node, we need to consider two scenarios:

• The number of flips required if the current node's evaluation needs to be true
• The number of flips required if the current node's evaluation needs to be false

Because we are dealing with a boolean operation, we must consider the operation represented by each non-leaf node when computing the number of flips.

Here's the intuition behind the solution:

• If we encounter a leaf node, the cost to make it true or false is straightforward: if the leaf node's current value is 0, it would take 0 flips to make it false and 1 flip to make it true, and vice versa for a leaf with a value of 1.

• For non-leaf nodes, the situation is more complex, and we need to consider the operation represented by the node and the costs associated with flipping each of its children to make it either true or false.

The core of the solution is a recursive depth-first search that, for each node, returns a tuple (cost_to_true, cost_to_false) where:

• cost_to_true is the minimum number of flips to make the current subtree evaluate to true.
• cost_to_false is the minimum number of flips to make the current subtree evaluate to false.

Depending on the node's operation, we combine the costs of its children according to the boolean logic of OR, AND, XOR, and NOT.

Finally, the answer will be the cost associated with the root node to make its evaluation equal to the result. In the provided solution, this is implemented as dfs(root)[int(result)].

Learn more about Tree, Depth-First Search, Dynamic Programming and Binary Tree patterns.

Solution Approach

The solution implements a recursive approach to traverse the tree and calculate the minimum number of flips required to achieve the desired result for each subtree. The algorithm uses Depth-First Search (DFS) which is a common tree traversal technique.

Here's how we implement the solution:

• We define a recursive function called dfs which takes a node as input and returns a tuple (cost_to_true, cost_to_false). This tuple represents the minimum number of flips required to make the subtree rooted at the given node evaluate to true and false, respectively.

• For a leaf node with value 0 or 1, the cost to make it true is its value (since 0 means false and 1 means true), and the cost to make it false is its complement (which is calculated by x ^ 1, where x is the value of the node).

• For a non-leaf node, we recursively get the costs for its left and right children. The combined cost depends on the operation the non-leaf node represents:

  • OR (2): The minimum number of flips required to make the result true is simply the sum of flips for both children to be true. To make the result false, we need to have at least one child as false, so we take the minimum of either child being false or both being false.
  • AND (3): The flips to make the result true are the minimum required to get one or both children true. To make the result false, both children must be false, so we add both children's cost_to_false.
  • XOR (4): To maintain the true result, either one child should be true and the other false, so we take the two minimums accordingly. To make the result false, both children need to have the same evaluation, so we consider either both true or both false.
  • NOT (5): This operation flips the outcome, so we swap the costs for true and false of the single child node.

• After calculating the costs for a node's children, we take the appropriate combination of costs as per the node's operation. Importantly, the solution ensures that results are mathematically minimized, taking advantage of the boolean nature of the problem to calculate the minimum number of steps.

• The base case for the recursion is when we encounter a None type for a child node; we return (inf, inf) since we don't perform operations on non-existent children.

• The function dfs(root)[int(result)] is used to extract the cost corresponding to the desired boolean result.

The solution leverages tuple unpacking, bitwise operations, and the recursive structure of the DFS algorithm, which makes it efficient and covers all edge cases due to its minimization logic at each step of the recursion.

Example Walkthrough

Let's illustrate the solution approach with a small example. Consider the following binary tree representing a boolean expression:

       2
      / \
     /   \
    5     3
   /     / \
  1     1   0

In this tree:

• The root node represents an OR operation (2).
• The left child of the root is a NOT operation (5), with a leaf child of 1 (true).
• The right child of the root is an AND operation (3), with two leaf children 1 (true) and 0 (false).

Let's say we want the final evaluated result of the tree to be true (1).

Here's how we walk through the solution approach:

1. At the root (OR operation), we need to evaluate both the left and right subtrees.
2. The left subtree is a NOT operation with a child leaf node of 1:
   • To make the NOT node true, since it's currently true, it would need to be flipped to false first, then NOT applied. That's 1 flip.
   • To make it false, we do nothing since NOT 1 is false. That's 0 flips.
3. The right subtree is an AND operation with two leaf children 1 and 0:
   • To make the AND node true, we need both children to be true. In this case, we need 1 flip for the child 0 to become 1.
   • To make it false, we can choose either of the children to be false, which in this case requires 0 flips since the right child is already 0.
4. Now, back at the root OR node, to make it true:
   • We can make either the left or the right subtree true. As per the calculated costs, making the left subtree true costs 1 flip, and making the right subtree true costs 1 flip as well. So, we would choose the minimum, which is 1 flip in this case.
   • We don't need to calculate the cost to make it false since the desired result is true.
5. The minimum number of flips to get to the result is hence 1 which corresponds to flipping the leaf node from 0 to 1 in the right AND subtree.

By following the approach outlined in the solution, we recursively calculate the minimum number of flips and choose the optimal path at each step to achieve the desired boolean value with the least number of flips.

Solution Implementation

Time and Space Complexity

The given code implements an algorithm to calculate the minimum number of flips necessary in a binary tree such that the value of the root node is equal to the desired result.

Time Complexity:

The time complexity of the algorithm is O(N), where N is the number of nodes in the binary tree. This is because we visit each node exactly once in a depth-first search (DFS) manner. For each node, we perform a constant-time operation regardless of its value, which either directly returns the values for leaves or calculates the minimum based on the values returned from the children for internal nodes.

Space Complexity:

The space complexity of the algorithm can be analyzed by considering the call stack for the recursive function dfs. In the worst-case scenario, the recursion depth is equal to the height of the tree. For a balanced binary tree, the height is O(log N), which would make the space complexity O(log N) due to the call stack. However, for a completely unbalanced tree (e.g., a linked list), the recursion could be as deep as O(N). Thus, the space complexity of the algorithm is O(H), where H is the height of the tree, and in the worst case, it is O(N).

Learn more about how to find time and space complexity quickly using problem constraints.