Distribute Coins is a problem that involves distributing coins among a binary tree's nodes. Each node has a certain number of coins, and the goal is to balance the number of coins in the tree by moving them between nodes. The problem typically asks for the minimum number of moves required to achieve a balanced state.
Example: Let's consider a binary tree with coins assigned to each node. The goal is to determine the minimum number of moves required to balance the number of coins in the tree.
# Node class definition
def __init__(self, val=0, left=None, right=None):
self.val = val
self.left = left
self.right = right
# Function to distribute coins and calculate minimum moves
moves = 0
if not node:
left = dfs(node.left)
right = dfs(node.right)
excess = node.val + left + right - 1
moves += abs(excess)
# Create a binary tree
root = TreeNode(3)
root.left = TreeNode(0)
root.right = TreeNode(0)
root.left.right = TreeNode(2)
root.left.right.left = TreeNode(1)
# Distribute coins and calculate minimum moves
min_moves = distributeCoins(root)
print("Minimum moves required:", min_moves)
# Output: 4
In this example, we define a
TreeNode class that represents a node in the binary tree. Each node has a value (
val), left child (
left), and right child (
We define the
distributeCoins function that takes the root of the binary tree as input and returns the minimum number of moves required to balance the coins in the tree.
distributeCoins function, we initialize a variable
moves to keep track of the minimum moves required.
We define a nested helper function
dfs (depth-first search) that performs a depth-first traversal of the tree. The
dfs function takes a node as input and returns the excess number of coins in that subtree.
dfs function, we handle the base case where the node is
None, in which case we return 0.
We recursively call
dfs on the left and right child nodes to calculate the excess number of coins in the left and right subtrees.
We calculate the excess in the current node by adding the coins in the node, left subtree, and right subtree, and subtracting 1 (as we want each node to have 1 coin).
We update the
moves variable by adding the absolute value of the excess, as it represents the number of moves required to balance the coins in the current subtree.
Finally, we return the excess value of the current node.
dfs function, we call
dfs on the root of the binary tree to start the traversal and calculate the minimum moves required.
We print the minimum moves, which represents the minimum number of moves required to balance the coins in the tree.
The time complexity of this solution is O(n), where n is the number of nodes in the binary tree, as we need to visit each node once. The space complexity is O(h), where h is the height of the binary tree, as the maximum depth of the call stack during the recursive traversal is determined by the height of the tree.
In this example, we reuse the same binary tree and the
distributeCoins function from the previous explanation.
The given binary tree has the following structure:
3 / \ 0 0 \ 2 / 1
We call the
distributeCoins function with the root of the binary tree.
The function calculates the minimum number of moves required to balance the coins in the tree and returns the result.
In this case, the minimum number of moves required to achieve a balanced state is 4.
The time complexity and space complexity of this solution remain the same as explained earlier: O(n) and O(h), respectively, where n is the number of nodes in the binary tree and h is the height of the tree.