787. Cheapest Flights Within K Stops
Description
There are n
cities connected by some number of flights. You are given an array flights
where flights[i] = [fromi, toi, pricei]
indicates that there is a flight from city fromi
to city toi
with cost pricei
.
You are also given three integers src
, dst
, and k
, return the cheapest price from src
to dst
with at most k
stops. If there is no such route, return -1
.
Example 1:
Input: n = 4, flights = [[0,1,100],[1,2,100],[2,0,100],[1,3,600],[2,3,200]], src = 0, dst = 3, k = 1 Output: 700 Explanation: The graph is shown above. The optimal path with at most 1 stop from city 0 to 3 is marked in red and has cost 100 + 600 = 700. Note that the path through cities [0,1,2,3] is cheaper but is invalid because it uses 2 stops.
Example 2:
Input: n = 3, flights = [[0,1,100],[1,2,100],[0,2,500]], src = 0, dst = 2, k = 1 Output: 200 Explanation: The graph is shown above. The optimal path with at most 1 stop from city 0 to 2 is marked in red and has cost 100 + 100 = 200.
Example 3:
Input: n = 3, flights = [[0,1,100],[1,2,100],[0,2,500]], src = 0, dst = 2, k = 0 Output: 500 Explanation: The graph is shown above. The optimal path with no stops from city 0 to 2 is marked in red and has cost 500.
Constraints:
1 <= n <= 100
0 <= flights.length <= (n * (n - 1) / 2)
flights[i].length == 3
0 <= fromi, toi < n
fromi != toi
1 <= pricei <= 104
- There will not be any multiple flights between two cities.
0 <= src, dst, k < n
src != dst
Solution
cheapest-flights-within-k-stops.py
class Solution:
def findCheapestPrice(self, n: int, flights: List[List[int]], src: int, dst: int, k: int) -> int:
pq = [(0, src, k + 1)]
graph = defaultdict(list)
seen = {}
for a, b, price in flights:
graph[a].append((b, price))
while pq:
weight, node, stops = heappop(pq)
if node == dst: return weight
if node in seen and seen[node] >= stops: continue
seen[node] = stops
if stops > 0:
for adj, adjW in graph[node]:
heappush(pq, (weight + adjW, adj, stops - 1))
return -1
cheapest-flights-within-k-stops.java
class Solution {
public int findCheapestPrice(int n, int[][] flights, int src, int dst, int K) {
// Build the adjacency matrix
int adjMatrix[][] = new int[n][n];
for (int[] flight: flights) {
adjMatrix[flight[0]][flight[1]] = flight[2];
}
// Shortest distances array
int[] distances = new int[n];
// Shortest steps array
int[] currentStops = new int[n];
Arrays.fill(distances, Integer.MAX_VALUE);
Arrays.fill(currentStops, Integer.MAX_VALUE);
distances[src] = 0;
currentStops[src] = 0;
// The priority queue would contain (node, cost, stops)
PriorityQueue<int[]> minHeap = new PriorityQueue<int[]>((a, b) -> a[1] - b[1]);
minHeap.offer(new int[]{src, 0, 0});
while (!minHeap.isEmpty()) {
int[] info = minHeap.poll();
int node = info[0], stops = info[2], cost = info[1];
// If destination is reached, return the cost to get here
if (node == dst) {
return cost;
}
// If there are no more steps left, continue
if (stops == K + 1) {
continue;
}
// Examine and relax all neighboring edges if possible
for (int nei = 0; nei < n; nei++) {
if (adjMatrix[node][nei] > 0) {
int dU = cost, dV = distances[nei], wUV = adjMatrix[node][nei];
// Better cost?
if (dU + wUV < dV) {
minHeap.offer(new int[]{nei, dU + wUV, stops + 1});
distances[nei] = dU + wUV;
}
else if (stops < currentStops[nei]) {
minHeap.offer(new int[]{nei, dU + wUV, stops + 1});
}
currentStops[nei] = stops;
}
}
}
return distances[dst] == Integer.MAX_VALUE? -1 : distances[dst];
}
}