DP on Trees Diameter of Tree in DSA C - Time & Space Complexity

Time Complexity: DP on Trees Diameter of Tree

O(n)

Understanding Time Complexity

We want to understand how long it takes to find the longest path in a tree using dynamic programming.

How does the time grow as the tree gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

int diameter = 0;

int dfs(int node, int parent, vector<vector<int>>& adj) {
    int max1 = 0, max2 = 0;
    for (int child : adj[node]) {
        if (child == parent) continue;
        int height = dfs(child, node, adj) + 1;
        if (height > max1) {
            max2 = max1;
            max1 = height;
        } else if (height > max2) {
            max2 = height;
        }
    }
    diameter = max(diameter, max1 + max2);
    return max1;
}

// Call dfs(0, -1, adj) to start

This code finds the diameter of a tree by exploring each node and calculating the longest paths down its children.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Recursive depth-first search (DFS) visiting each node once.
• How many times: Each node is visited exactly one time, and each edge is checked once in the adjacency list.

How Execution Grows With Input

As the number of nodes grows, the DFS visits each node and edge once, so the work grows roughly in direct proportion to the number of nodes and edges.

Input Size (n)	Approx. Operations
10	About 10 nodes and 9 edges visited
100	About 100 nodes and 99 edges visited
1000	About 1000 nodes and 999 edges visited

Pattern observation: The operations increase roughly linearly as the tree size increases.

Final Time Complexity

Time Complexity: O(n)

This means the time to find the diameter grows linearly with the number of nodes in the tree.

Common Mistake

[X] Wrong: "The diameter calculation takes quadratic time because it checks pairs of nodes."

[OK] Correct: The code uses DFS to visit each node once, not checking all pairs separately, so it runs in linear time.

Interview Connect

Understanding this linear time approach shows you can handle tree problems efficiently, a key skill in many coding challenges.

Self-Check

"What if we changed the tree to a graph with cycles? How would the time complexity change?"

Practice

1. What is the diameter of a tree in the context of dynamic programming on trees?

easy

A. The height of the tree from the root

B. The number of nodes in the tree

C. The sum of all edge weights in the tree

D. The longest path between any two nodes in the tree

Solution

1. Step 1: Understand the definition of tree diameter

   The diameter is the longest path between any two nodes in the tree, not just from the root.

2. Step 2: Differentiate from other tree properties

   Height is from root to leaf, sum of edges is total weight, and node count is size, none represent longest path.

3. Final Answer:

   The longest path between any two nodes in the tree -> Option D

4. Quick Check:

   Diameter = Longest path [OK]

Hint: Diameter is longest path between any two nodes [OK]

Common Mistakes:

• Confusing diameter with height
• Thinking diameter is number of nodes
• Assuming diameter is sum of all edges

2. Which of the following is the correct way to declare an adjacency list for a tree with n nodes in C?

easy

A. int adj[n][n];

B. int* adj[n];

C. int adj[n];

D. int adjList[n][2];

Solution

1. Step 1: Understand adjacency list structure

   An adjacency list uses an array of pointers to lists (or arrays) of neighbors, so int* adj[n]; is correct.

2. Step 2: Differentiate from other declarations

   int adj[n][n]; is adjacency matrix, int adj[n]; is a simple array, int adjList[n][2]; is edge list, not adjacency list.

3. Final Answer:

   int* adj[n]; -> Option B

4. Quick Check:

   Adjacency list uses array of pointers [OK]

Hint: Adjacency list uses array of pointers to neighbors [OK]

Common Mistakes:

• Using 2D array as adjacency list
• Using simple array for adjacency
• Confusing edge list with adjacency list

3. Consider the following C code snippet to find the diameter of a tree. What will be the output if the tree has edges: 1-2, 2-3, 2-4?

int diameter = 0;
int dfs(int node, int parent, vector<int> adj[]) {
    int max1 = 0, max2 = 0;
    for (int child : adj[node]) {
        if (child == parent) continue;
        int height = dfs(child, node, adj) + 1;
        if (height > max1) {
            max2 = max1;
            max1 = height;
        } else if (height > max2) {
            max2 = height;
        }
    }
    diameter = max(diameter, max1 + max2);
    return max1;
}
// Call dfs(1, -1, adj) and then print diameter;

medium

A. 2

B. 3

C. 1

D. 4

Solution

1. Step 1: Trace DFS from node 1

   From node 1, child is 2. DFS(2) explores children 3 and 4. Heights from 3 and 4 are 1 each.

2. Step 2: Calculate diameter updates

   At node 2, max1=1, max2=1, diameter updated to 2. At node 1, max1=2, max2=0, diameter remains 2.

3. Final Answer:

   2 -> Option A

4. Quick Check:

   Longest path length = 2 edges [OK]

Hint: Diameter = sum of two longest child heights [OK]

Common Mistakes:

• Counting nodes instead of edges
• Not updating diameter correctly
• Ignoring parent to avoid cycles

4. The following code attempts to compute the diameter of a tree but produces incorrect results. What is the bug?

int diameter = 0;
int dfs(int node, int parent, vector<int> adj[]) {
    int max1 = 0, max2 = 0;
    for (int child : adj[node]) {
        int height = dfs(child, node, adj) + 1;
        if (height > max1) {
            max2 = max1;
            max1 = height;
        } else if (height > max2) {
            max2 = height;
        }
    }
    diameter = max(diameter, max1 + max2);
    return max1;
}
// Called with dfs(1, -1, adj);

medium

A. Incorrect initialization of diameter variable

B. Returning max2 instead of max1 from DFS

C. Missing check to skip parent node in DFS

D. Not adding 1 to height in DFS call

Solution

1. Step 1: Identify missing parent check in DFS loop

   The code does not skip the parent node, causing infinite recursion or wrong heights.

2. Step 2: Explain impact of missing check

   Without skipping parent, DFS revisits parent, causing incorrect diameter calculation or stack overflow.

3. Final Answer:

   Missing check to skip parent node in DFS -> Option C

4. Quick Check:

   Skip parent to avoid cycles [OK]

Hint: Always skip parent node in DFS to prevent cycles [OK]

Common Mistakes:

• Forgetting to skip parent node
• Returning wrong height value
• Incorrect diameter initialization

5. Given a weighted tree with edges: (1-2, 3), (2-3, 4), (2-4, 2), how would you modify the diameter calculation to find the longest weighted path? Choose the correct approach.

hard

A. Add edge weights to height calculations and update diameter with weighted sums

B. Ignore weights and use number of edges as diameter

C. Multiply edge weights instead of adding

D. Use BFS twice without considering weights

Solution

1. Step 1: Incorporate edge weights in DFS height calculation

   Instead of adding 1 for each edge, add the actual edge weight to the height returned by DFS.

2. Step 2: Update diameter with sum of two largest weighted heights

   Keep track of two largest weighted heights from children and update diameter with their sum.

3. Final Answer:

   Add edge weights to height calculations and update diameter with weighted sums -> Option A

4. Quick Check:

   Weighted diameter sums edge weights [OK]

Hint: Add weights to heights, sum two largest for diameter [OK]

Common Mistakes:

• Ignoring weights in diameter calculation
• Multiplying weights instead of adding
• Using unweighted BFS for weighted tree