Problem Description

Given a binary tree, where each node contains an integer, we are asked to find the largest absolute difference in value between two nodes where one node is an ancestor of the other. In other words, if we pick any node a as an ancestor, and any node b as a descendant of a, what is the maximum absolute difference between a.val and b.val (|a.val - b.val|) that we can find in the tree?

An important detail to note is that a node can be considered an ancestor of itself, leading to a minimum absolute difference of 0 in such a scenario. The problem is focusing on finding the maximum difference, hence we need to look for pairs of ancestor and descendant nodes where this difference is the largest.

Flowchart Walkthrough

Let's use the flowchart to analyze the appropriate algorithm for solving LeetCode problem 1026. Maximum Difference Between Node and Ancestor. Here’s a step-by-step determination using the given flowchart:

Is it a graph?

• Yes: The structure provided is a binary tree, which is a type of graph where each node has at most two children.

Is it a tree?

• Yes: Specifically, the problem is defined on a binary tree.

DFS

• Given the nature of the tree (it is indeed a tree), the next logical step is to use Depth-First Search (DFS).

Conclusion: The flowchart leads us directly to utilizing a Depth-First Search (DFS) for this tree-based problem, as DFS is well-suited for exploring all paths and finding the maximum difference between a node and its ancestors. This approach allows us to efficiently traverse the tree, maintaining current maximum and minimum values as we move from root to leaves, comparing and updating the required maximum difference.

Intuition

The intuition behind the solution is that we can find the maximum difference by thoroughly searching through the tree. We do this using a depth-first search (DFS) algorithm, which will allow us to explore each branch of the tree to its fullest extent before moving on to the next branch.

During the traversal, we keep track of the minimum (mi) and maximum (mx) values encountered along the path from the root to the current node. At each node, we calculate the absolute difference between root.val and both the mi and mx values, updating the global maximum ans if we find a larger difference.

The core idea is to track the range of values (minimum and maximum) on the path from the root to the current node because this range will allow us to compute the required maximum absolute difference at each step. By the time we complete our traversal, we will have examined all possible pairs of ancestor and descendant nodes and thus found the maximum difference.

To implement this, we use a recursive helper function dfs(root, mi, mx) that performs a depth-first search on the binary tree. The mi and mx parameters keep track of the minimum and maximum values respectively, seen from the root to the current node. The function also updates a nonlocal variable ans, which keeps track of the maximum difference found so far.

Finally, we initiate our DFS with the root node and its value as both the initial minimum and maximum, and after completing the traversal, we return the value stored in ans, which will be the maximum ancestor-difference that we were tasked to find.

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

Solution Approach

The solution to this problem involves a recursive depth-first search (DFS) algorithm to traverse the binary tree. The critical aspect of the approach is to maintain two variables, mi and mx, to record the minimum and maximum values found along the path from the root node to the current node.

Here is a step-by-step breakdown of the implementation details:

1. Define a recursive helper function dfs(root, mi, mx) that will be used for DFS traversal of the tree.
2. If the current root is None, which means we've reached a leaf node's child, we return, as there are no more nodes to process in this path.
3. The helper function is designed to continuously update a nonlocal variable ans, which holds the maximum absolute difference found.
4. At each node, we compare and update ans with the absolute difference of the current node's value root.val with both the minimum (mi) and maximum (mx) values seen so far along the path from the root.
5. We perform this comparison using max(ans, abs(mi - root.val), abs(mx - root.val)).
6. After updating ans, we also update mi and mx for the recursive calls on the children nodes, setting mi to min(mi, root.val) and mx to max(mx, root.val). This ensures that as we go deeper into the tree, our range [mi, mx] remains updated with the smallest and largest values seen along the path.
7. Recursive calls are then made to continue the DFS traversal on the left child dfs(root.left, mi, mx) and the right child dfs(root.right, mi, mx) of the current node.

The main function initializes the variable ans to 0 and then calls dfs(root, root.val, root.val). We start with both mi and mx as the root's value, since initially, the root is the only node in the path. The implementation leverages the default argument-passing mechanism in Python, where every child node receives the current path's minimum and maximum values to keep the comparison going.

After the completion of the DFS traversal, the ans variable, which was kept up-to-date during the traversal, will contain the final result—the maximum difference. The function finally returns ans.

The primary data structure used in this implementation is the binary tree itself. No additional data structures are needed because the recursion stack implicitly manages the traversal, and the updating of minimum and maximum values is done using integer variables. This efficient use of space and recursive traversal makes it a neat and effective solution.

Example Walkthrough

Let's consider a small binary tree to illustrate the solution approach. Our binary tree is as follows:

1       8
2      / \
3     3   10
4    / \    \
5   1   6    14
6      / \   / 
7     4   7 13  

We want to find the maximum absolute difference between the values of any two nodes where one is an ancestor of the other.

We begin by calling the recursive function dfs on the root node with value 8. We start with mi = mx = 8 since the root is both the minimum and maximum of the path consisting of just itself.

1. The dfs function is first called with root.val = 8, mi = 8, mx = 8. We are at the root.

2. Explore left child (3). Call dfs(3, min(8,3), max(8,3)):

   • Now mi = 3, mx = 8.
   • Update potential answer compare with previous ans:
     • max(0, abs(3 - 8), abs(8 - 3))
     • ans = 5

3. Go down to the left child of 3, node 1. Call dfs(1, min(3,1), max(8,1)):

   • Here, mi = 1, mx = 8.
   • Update answer:
     • max(5, abs(1 - 8), abs(8 - 1))
     • ans = 7

   Node 1 is a leaf; the traversal will go back up.

4. The other child of 3 is 6. Call dfs(6, min(3,6), max(8,6)):

   • mi = 3, mx = 8.
   • Update answer:
     • max(7, abs(3 - 6), abs(8 - 6))
     • ans remains 7.

5. Node 6 has a left child 4. Call dfs(4, min(3,4), max(8,4)):

   • mi = 3, mx = 8.
   • Update answer:
     • max(7, abs(3 - 4), abs(8 - 4))
     • ans remains 7.

   Since 4 is a leaf node, we go back up.

6. Node 6 also has right child 7. Call dfs(7, min(3,7), max(8,7)):

   • mi = 3, mx = 8.
   • Update answer:
     • max(7, abs(3 - 7), abs(8 - 7))
     • ans remains 7.

   Node 7 is also a leaf; traverse back up to 3, then to 8.

7. Now explore right child (10) of the root. Call dfs(10, min(8,10), max(8,10)):

   • mi = 8, mx = 10.
   • Update answer:
     • max(7, abs(8 - 10), abs(10 - 8))
     • ans remains 7.

8. Node 10 has right child 14. Call dfs(14, min(8,14), max(10,14)):

   • mi = 8, mx = 14.
   • Update answer:
     • max(7, abs(8 - 14), abs(14 - 8))
     • ans becomes 14 - 8 = 6 but since our current ans = 7, there is no update.

9. Node 14 has a left child 13. Call dfs(13, min(8,13), max(14,13)):

   • mi = 8, mx = 14.
   • Update answer:
     • max(7, abs(8 - 13), abs(14 - 13))
     • ans remains 7 because the differences 5 and 1 are smaller than the current ans.

After the recursive depth-first search completes, we find that the maximum absolute difference is 7, which comes from the difference between nodes 8 (ancestor) and 1 (descendant).

Solution Implementation

Time and Space Complexity

The given Python function maxAncestorDiff computes the maximum difference between the values of any two nodes with an ancestor/descendant relationship in a binary tree.

Time Complexity:

The time complexity of the function is O(N), where N is the number of nodes in the binary tree. This is because the function performs a depth-first search (DFS), visiting each node exactly once. During each visit, it performs a constant amount of work by updating the minimum and maximum values encountered so far and comparing them to the current node's value.

Space Complexity:

The space complexity of the function is O(H), where H is the height of the binary tree. This complexity comes from the call stack used for recursion during DFS. In the worst case of a skewed tree, where the tree takes the form of a linked list (either every node has only a right child or only a left child), the height H would be equal to N, leading to a worst-case space complexity of O(N). For a balanced tree, the space complexity would be O(log N), as the height H would be logarithmic in the number of nodes N.

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