There exists an undirected and initially unrooted tree with n nodes indexed from 0 to n - 1. You are given the integer n and 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.
Each node has an associated price. You are given an integer array price, where price[i] is the price of the ith node.
The price sum of a given path is the sum of the prices of all nodes lying on that path.
The tree can be rooted at any node root of your choice. The incurred cost after choosing root is the difference between the maximum and minimum price sum amongst all paths starting at root.
Return the maximum possible costamongst all possible root choices.
Example 1:
Input: n = 6, edges = [[0,1],[1,2],[1,3],[3,4],[3,5]], price = [9,8,7,6,10,5]
Output: 24
Explanation: The diagram above denotes the tree after rooting it at node 2. The first part (colored in red) shows the path with the maximum price sum. The second part (colored in blue) shows the path with the minimum price sum.
- The first path contains nodes [2,1,3,4]: the prices are [7,8,6,10], and the sum of the prices is 31.
- The second path contains the node [2] with the price [7].
The difference between the maximum and minimum price sum is 24. It can be proved that 24 is the maximum cost.
Example 2:
Input: n = 3, edges = [[0,1],[1,2]], price = [1,1,1]
Output: 2
Explanation: The diagram above denotes the tree after rooting it at node 0. The first part (colored in red) shows the path with the maximum price sum. The second part (colored in blue) shows the path with the minimum price sum.
- The first path contains nodes [0,1,2]: the prices are [1,1,1], and the sum of the prices is 3.
- The second path contains node [0] with a price [1].
The difference between the maximum and minimum price sum is 2. It can be proved that 2 is the maximum cost.
Constraints:
1 <= n <= 105
edges.length == n - 1
0 <= ai, bi <= n - 1
edges represents a valid tree.
price.length == n
1 <= price[i] <= 105
Solutions
Solution 1
Thinking
A path costs the sum of node prices minus the smaller endpoint; we want the maximum over all paths. Prices are positive, so this is the path sum minus one endpoint. Enumerating paths is \(O(n^2)\).
Tree DP keeps two values per subtree: the longest downward chain \(a\) that still includes the far endpoint, and the longest chain \(b\) after dropping that endpoint. Combining \(a\) with a child's \(d\), or \(b\) with a child's \(c\), covers the best path through the current node.
A path costs the sum of node prices minus the smaller endpoint, and prices are positive, so that is the path sum minus one endpoint. With \(n \le 10^5\), enumerating paths is \(O(n^2)\), and recursing from the root for the two downward chains is too deep: a chain makes the call depth \(n\).
Each node keeps two values: the longest downward chain \(a\) that still includes the far endpoint, and the longest chain \(b\) after that endpoint is dropped. The best path through the node joins one of those chains with a child's chain of the other kind, \(a\) with \(d\) or \(b\) with \(c\).
An explicit stack of \((\textit{node}, \textit{parent}, \textit{state})\) runs the postorder. On entry we push the exit marker and then the children, and on exit we update the answer from the children's two chains and store this node's \(a\) and \(b\).