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Maximum Points After Collecting Coins From All Nodes

Hard
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DE Shaw
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Topics:
TreesDynamic ProgrammingRecursion

There exists an undirected tree rooted at node 0 with n nodes labeled from 0 to n - 1. You are given a 2D integer array edges of length n - 1, where edges[i] = [ai, bi] indicates that there is an edge between nodes ai and bi in the tree. You are also given a 0-indexed array coins of size n where coins[i] indicates the number of coins in the vertex i, and an integer k.

Starting from the root, you have to collect all the coins such that the coins at a node can only be collected if the coins of its ancestors have been already collected.

Coins at nodei can be collected in one of the following ways:

  • Collect all the coins, but you will get coins[i] - k points. If coins[i] - k is negative then you will lose abs(coins[i] - k) points.
  • Collect all the coins, but you will get floor(coins[i] / 2) points. If this way is used, then for all the nodej present in the subtree of nodei, coins[j] will get reduced to floor(coins[j] / 2).

Return the maximum points you can get after collecting the coins from all the tree nodes.

Example 1:

Input: edges = [[0,1],[1,2],[2,3]], coins = [10,10,3,3], k = 5
Output: 11                        
Explanation: 
Collect all the coins from node 0 using the first way. Total points = 10 - 5 = 5.
Collect all the coins from node 1 using the first way. Total points = 5 + (10 - 5) = 10.
Collect all the coins from node 2 using the second way so coins left at node 3 will be floor(3 / 2) = 1. Total points = 10 + floor(3 / 2) = 11.
Collect all the coins from node 3 using the second way. Total points = 11 + floor(1 / 2) = 11.
It can be shown that the maximum points we can get after collecting coins from all the nodes is 11. 

Example 2:

Input: edges = [[0,1],[0,2]], coins = [8,4,4], k = 0
Output: 16
Explanation: 
Coins will be collected from all the nodes using the first way. Therefore, total points = (8 - 0) + (4 - 0) + (4 - 0) = 16.

Constraints:

  • n == coins.length
  • 2 <= n <= 105
  • 0 <= coins[i] <= 104
  • edges.length == n - 1
  • 0 <= edges[i][0], edges[i][1] < n
  • 0 <= k <= 104

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:

  1. What are the constraints on the number of nodes `n` in the tree (maximum and minimum)?
  2. Can the `coins[i]` values be zero or negative?
  3. Is the tree rooted or unrooted? If unrooted, can I choose the root?
  4. Is the tree represented as an adjacency list or adjacency matrix, and can I assume the input format will always be valid?
  5. After halving the coins, should I use floor or ceiling division?

Brute Force Solution

Approach

The brute force approach to this coin collection problem explores absolutely every possible path through the tree. We consider whether or not to collect coins at each node and then explore all the resulting options from that decision.

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

  1. Start at the root node of the tree.
  2. Consider the first choice: collect the coins at the current node or skip them.
  3. If we collect the coins, add those points to our total and move to the next available node.
  4. If we skip the coins, we don't add any points, and move to the next available node.
  5. At the next node, repeat the same choice: collect or skip.
  6. Keep doing this until we've visited every node in the tree, making a decision at each one.
  7. This gives us one possible total score.
  8. Now, go all the way back to the root and repeat the process, but this time, make a different initial choice (if we collected the first time, now skip, and vice-versa).
  9. Again, explore all possible paths by making collect/skip decisions at each node.
  10. Continue generating every single combination of collecting and skipping coins at each node until all possibilities are exhausted.
  11. Compare the total scores from every possibility, and choose the highest one. That's our maximum possible points.

Code Implementation

def maximum_points_brute_force(
    node_index,
    coins,
    graph
):

    # If node_index is out of bounds, return 0
    if node_index is None:
        return 0

    # Option 1: Collect coins at the current node
    collect_points = coins[node_index]

    max_collect_path = 0
    for neighbor in graph[node_index]:
        max_collect_path += maximum_points_brute_force(
            neighbor,
            coins,
            graph
        )
    collect_points += max_collect_path

    # Option 2: Skip coins at the current node
    skip_points = 0

    max_skip_path = 0
    for neighbor in graph[node_index]:
        max_skip_path += maximum_points_brute_force(
            neighbor,
            coins,
            graph
        )
    skip_points += max_skip_path

    # Choose the path that gives maximum points.
    return max(collect_points, skip_points)

def solve():
    coins = [1, 2, 3, 4, 5]
    graph = {
        0: [1, 2],
        1: [3],
        2: [4],
        3: [],
        4: []
    }

    #Start traversal at root
    return maximum_points_brute_force(
        0,
        coins,
        graph
    )

Big(O) Analysis

Time Complexity
O(2^n)The given brute force approach explores all possible combinations of collecting or skipping coins at each node in the tree. For each of the n nodes, we have two choices: collect or skip. This leads to a binary decision tree where each level corresponds to a node. Therefore, the total number of possible paths (and thus the total number of computations) is 2 multiplied by itself n times, or 2^n. The algorithm essentially iterates through all these 2^n possibilities to find the maximum score. Thus the time complexity is O(2^n).
Space Complexity
O(N)The brute force approach explores all possible paths using recursion. At each node, a decision is made to either collect coins or skip them, leading to two recursive calls in the worst case. The maximum depth of the recursion will be N, where N is the number of nodes in the tree. Each recursive call adds a new frame to the call stack, requiring memory to store local variables and the return address. Therefore, the space complexity is O(N) due to the maximum depth of the call stack.

Optimal Solution

Approach

This problem involves figuring out the best way to collect coins from a tree-like structure. The smart approach avoids checking every possibility by working from the ground up and making decisions based on the best immediate reward, which will lead to the best overall score.

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

  1. Imagine each point in the tree as a location where you can collect coins. You can either collect the coins or skip them.
  2. Start at the very bottom of the tree, the 'leaves'. For each leaf, figure out if you should take the coins or skip them, and record the best choice.
  3. Now, move up to the next level of points in the tree. For each of these points, consider two options: taking the coins and skipping them.
  4. If you take the coins at a point, you cannot take the coins from its immediate children (the points directly below it). Calculate the total coins you'd get in this scenario, including the best choices you already made for the grandchildren of this point.
  5. If you skip the coins at a point, you *can* take the coins from its immediate children. Again, calculate the total coins you'd get in this scenario, using the best choices already made for the children.
  6. Compare the 'take' scenario and the 'skip' scenario, and record the best one for that point. This becomes the best choice for that point when considering points further up the tree.
  7. Repeat this process, moving up level by level, until you reach the very top of the tree. The best choice recorded for the top point is the maximum number of coins you can collect from the entire tree.

Code Implementation

def maximum_points_after_collecting_coins(coins, edges):
    number_of_nodes = len(coins)
    graph = [[] for _ in range(number_of_nodes)]
    for edge_start, edge_end in edges:
        graph[edge_start].append(edge_end)
        graph[edge_end].append(edge_start)

    memo = {}

    def solve(node, parent):
        if (node, parent) in memo:
            return memo[(node, parent)]

        # Option 1: Take the coins from the current node
        take_coins = coins[node]
        for child in graph[node]:
            if child != parent:
                # Cannot take coins from children, so skip them
                take_coins += solve(child, node)[1]

        # Option 2: Skip the coins from the current node
        skip_coins = 0
        for child in graph[node]:
            if child != parent:
                # Can take or skip coins from children, choose the best
                skip_coins += max(solve(child, node))

        memo[(node, parent)] = (take_coins, skip_coins)
        return take_coins, skip_coins

    # Start DFS from node 0 (arbitrary root)
    # with no parent (-1)
    result = max(solve(0, -1))
    return result

Big(O) Analysis

Time Complexity
O(n)The algorithm traverses the tree in a bottom-up manner, visiting each node exactly once to determine the maximum coins achievable by either taking or skipping the coins at that node. The key is that for each node, the computation involves a constant amount of work: comparing two options (take or skip) based on precomputed results from its children. Since each node is processed only once, and the work per node is constant, the overall time complexity is directly proportional to the number of nodes in the tree, which is 'n'.
Space Complexity
O(N)The algorithm uses a bottom-up approach on a tree, which implies storing intermediate results for each node. Specifically, for each node, the algorithm records the best outcome of taking coins versus skipping them. Since there are N nodes in the tree, we need to store two values (take coins, skip coins) for each of the N nodes. Thus, the auxiliary space used scales linearly with the number of nodes, N, resulting in O(N) space complexity.

Edge Cases

CaseHow to Handle
Null or empty tree (no nodes)Return 0 if the tree is null or empty as there are no coins to collect.
Single node treeReturn the value of coins[0] as we can only collect from the single node.
Tree with all coins values as 0Return 0 as no matter how we traverse, we collect 0 coins.
All coins values are the sameThe algorithm should still function correctly, potentially demonstrating symmetric optimal paths.
Coins values are very large, potentially leading to integer overflow after multiple halvingsUse long data types or consider modulo operations if applicable to prevent overflow.
Highly skewed tree (e.g., a linked list)The algorithm's performance might degrade in highly skewed trees, requiring potential optimization for such cases.
Deeply nested tree, potentially causing stack overflow with naive recursionConsider using iterative approach with explicit stack or memoization to avoid stack overflow.
Tree with negative coin values (although problem description implies non-negative)The algorithm should handle this gracefully, possibly by modifying the base cases or the DP transitions to correctly handle negative coin values potentially subtracting from the total.