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Problem solution · Java

Optimize Water Distribution in a Village

Optimize Water Distribution in a Village: a Java solution using breadth-first search. Learn the idea, check the complexity, and read the full code, with credit to walkccc LeetCode Solutions.

Technique
Breadth-first search
Source
walkccc LeetCode Solutions
Length
47 lines
Start with the idea.

Try the problem first. If you get stuck, read the approach below, then write your own solution. The full code is at the bottom.

Approach

Breadth-first search

For Optimize Water Distribution in a Village, the implementation explores reachable states in layers, which is the standard shape for unweighted shortest paths and minimum-step transitions.

  1. Model each valid configuration as a state and each legal move as an edge.
  2. Seed the queue with the starting state and mark it immediately.
  3. Expand each state once, recording distance or reachability for unseen neighbours.

Code notes

  • 47 lines of Java from the credited upstream file 1168.java.
  • The implementation visibly relies on sequence storage, hash lookup, ordered lookup, work queue.
  • 5 loop blocks detected.

Complexity

Verify that each state and transition is processed only a bounded number of times; that determines the traversal cost.

Check the problem constraints before deciding whether this complexity will pass.

Source

Code and credit

This code comes from walkccc LeetCode Solutions by P.-Y. Chen (walkccc) and is used under the MIT licence.

Open problem ↗Original source ↗
Full codeOptimize Water Distribution in a Village · JavaJava
Use this to learn the idea, then write your own version.
class Solution {  public int minCostToSupplyWater(int n, int[] wells, int[][] pipes) {    int ans = 0;    List<Pair<Integer, Integer>>[] graph = new List[n + 1];    Queue<Pair<Integer, Integer>> minHeap =        new PriorityQueue<>(Comparator.comparingInt(Pair::getKey)); // (d, u)     for (int i = 0; i <= n; ++i)      graph[i] = new ArrayList<>();     for (int[] pipe : pipes) {      final int u = pipe[0];      final int v = pipe[1];      final int w = pipe[2];      graph[u].add(new Pair<>(v, w));      graph[v].add(new Pair<>(u, w));    }     // Connect virtual 0 with nodes 1 to n.    for (int i = 0; i < n; ++i) {      graph[0].add(new Pair<>(i + 1, wells[i]));      minHeap.offer(new Pair<>(wells[i], i + 1));    }     Set<Integer> mst = new HashSet<>(Arrays.asList(0));     while (mst.size() < n + 1) {      final int d = minHeap.peek().getKey();      final int u = minHeap.poll().getValue();      if (mst.contains(u))        continue;      // Add the new vertex.      mst.add(u);      ans += d;      // Expand if possible.      for (Pair<Integer, Integer> pair : graph[u]) {        final int v = pair.getKey();        final int w = pair.getValue();        if (!mst.contains(v))          minHeap.offer(new Pair<>(w, v));      }    }     return ans;  }}

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