Problem Description

This LeetCode problem requires finding the cheapest flight route from one city to another with certain constraints. We have n cities and a list of flights where each flight is represented by [from_i, to_i, price_i]. This indicates a flight from city from_i to city to_i at the cost price_i. Given additional parameters src, dst, and k, the goal is to find the minimum cost to travel from the source city src to the destination city dst with at most k stops in between. If no such route exists, the function should return -1.

Flowchart Walkthrough

To analyze the algorithm best suited for solving LeetCode problem 787, Cheapest Flights Within K Stops, we will use the Algorithm Flowchart. Here's the detailed step-by-step process:

Is it a graph?

• Yes: This problem models flights between cities as a graph where cities are nodes and flights are directed edges with weights representing costs.

Is it a tree?

• No: The graph can have cycles as flights can go back and forth between the same cities, and multiple routes can exist between cities.

Is the problem related to directed acyclic graphs (DAGs)?

• No: The presence of potential cycles in the city-flight relationships (due to return flights and multiple pathways) rules out DAGs.

Is the problem related to shortest paths?

• Yes: The goal is to find the cheapest (least cost) route from a starting city to a destination, which aligns with finding a shortest path in terms of cost minimization.

Is the graph weighted?

• Yes: Each flight has a cost associated with it, making the graph weighted.

Conclusion: Following the flowchart leads us to use Depth-First Search due to the nature of exploring all possible paths up to K stops, thus effectively managing to explore various flight combinations to achieve the minimum cost, which can be optimized further with additional constraints like limiting the number of stops. DFS is suitable in cases where all paths need to be explored to certain conditions (like a fixed number of stops), which is a vital aspect of this problem.

Intuition

To solve this problem, we use a variation of the Bellman-Ford algorithm which is suitable for finding shortest paths in graphs with edge weights that could be negative (not the case here) and can handle queries for paths with a limited number of edges.

We initialize a list dist of size n with a high value representing infinity since we are looking for the minimum cost. This list will keep track of the minimum cost to reach each city. The entry for the source city src is set to 0 since it costs nothing to stay at the same city.

The algorithm will go through the flights at most k+1 times (the number of stops plus the initial city). On each iteration, we make a copy of the dist list called backup. This is important to ensure that updates in the current round do not affect the other updates in the same round since we are only allowed to use k stops.

For each flight [f, t, p] in flights, we check if the current known cost to city t is greater than the cost to city f plus the price p of the flight from f to t. If it is, we update the cost for city t in the dist list with the new lower cost. This represents a relaxation step where we relax the cost to get to t via f if it is possible to get there more cheaply.

After k+1 iterations, the dist list contains the minimum costs of reaching each city from the source city with up to k stops. The value in dist[dst] is the cheapest price to get to the destination dst. However, if the value at dist[dst] is still infinity (represented by INF), it means there was no possible route within the given number of stops, hence we return -1.

Learn more about Depth-First Search, Breadth-First Search, Graph, Dynamic Programming, Shortest Path and Heap (Priority Queue) patterns.

Solution Approach

The implementation of the solution uses dynamic programming to iteratively update the cheapest prices for reaching each city within the constraint of at most k stops. Here's a step-by-step walkthrough of the algorithm:

1. Initialization: An array dist is initialized to store the cheapest prices to each city. It is filled with INF (a large number indicating infinity) to represent that initially, all destinations are unreachable. The price to reach the src city is set to 0 because it costs nothing to start from there.

2. Algorithm: The core part of the algorithm runs for k+1 iterations, where k is the maximum number of stops allowed. The extra iteration is because a journey from src to dst with k stops consists of k+1 flights.

3. Backup Array: In each iteration, a backup of the dist array is created. This is crucial because updates to the dist array within a single iteration must be based on the state of the array from the previous iteration, not the current one where some values have already been updated.

4. Relaxation: For each flight [f, t, p], where f is the starting city, t is the target city, and p is the price of the flight, the algorithm checks if the current known price to city t can be improved by taking the flight from city f. The relaxation condition is:

   if dist[t] > backup[f] + p:
       dist[t] = backup[f] + p

   If the price to reach city t (dist[t]) is higher than the price to reach city f (backup[f]) plus the price of the flight p, the cheaper price is updated in dist[t].

5. Returning the result: After k+1 iterations, the algorithm looks at the price recorded in dist[dst], which is the minimum cost to reach destination dst from source src within k stops. If this price is still INF, it means no such route was found within the limit of stops, and -1 is returned. Otherwise, the cheapest price found is returned.

This approach efficiently utilizes the Bellman-Ford algorithm's concept of relaxing edges but modifies it to account for the number of stops constraint by limiting the number of relaxation iterations.

Example Walkthrough

Let's use a small example to illustrate the solution approach described above. Consider the following scenario where n = 4 cities, src = 0 (source city), dst = 3 (destination city), k = 1 (maximum 1 stop allowed), and the flights are represented by a list of [from_i, to_i, price_i] as follows: flights = [[0, 1, 100], [1, 3, 100], [0, 2, 500], [2, 3, 100]].

In this example, there are direct flights from city 0 to city 1 and city 2, and from city 1 and city 2 to city 3 with the respective prices of 100 and 500.

1. Initialization: We start by initializing the dist array with INF values for all cities except the src city: dist = [0, INF, INF, INF].

2. Algorithm: We will iterate k+1 times, which in this case is 2 times (1 stop + 1 initial trip).

3. Backup Array: In each iteration, a backup of dist is made.

4. Relaxation:

   • First iteration:
     • Flight [0, 1, 100] (from city 0 to city 1): Checks if dist[1] > backup[0] + 100. Since INF > 0 + 100, update dist[1] to 100.
     • Flight [1, 3, 100] (from city 1 to city 3): Checks if dist[3] > backup[1] + 100. Since INF > INF + 100, no update is made.
     • Flight [0, 2, 500] (from city 0 to city 2): Checks if dist[2] > backup[0] + 500. Since INF > 0 + 500, update dist[2] to 500.
     • Flight [2, 3, 100] (from city 2 to city 3): Checks if dist[3] > backup[2] + 100. Since INF > INF + 100, no update is made.
   • Second iteration (with updated dist from first iteration; new backup is made):
     • Flight [0, 1, 100]: Since dist[1] is already at its lowest from the first iteration, no changes occur.
     • Flight [1, 3, 100]: Checks if dist[3] > backup[1] + 100. Since INF > 100 + 100, update dist[3] to 200.
     • Flight [0, 2, 500]: Since dist[2] is already at its lowest from the first iteration, no changes occur.
     • Flight [2, 3, 100]: Checks if dist[3] > backup[2] + 100. Since 200 > 500 + 100, no update is made. (Showing that we found a cheaper way to get to city 3 during this iteration)

5. Returning the result: After 2 iterations, we look at dist[dst], which is dist[3]. The value is 200, which is the cheapest price to reach city dst from src within 1 stop. This value is returned as the result. If dist[dst] had still been INF, then we would return -1, indicating no possible route within the given stop constraint.

Solution Implementation

Time and Space Complexity

Time Complexity

The given code defines a function that finds the cheapest price for a flight from a source to a destination with at most k stops. The main operations in this code are as follows:

1. We initialize the dist array of size n with INF to represent the minimum cost to reach each node. This operation has a time complexity of O(n).

2. We then run a loop k+1 times to account for the fact that we can make at most k stops, which means we are considering paths with at most k+1 edges. The loop operation involves iterating through the list of flights.

3. Inside the loop, a .copy() operation is performed on the dist array, which again takes O(n) time.

4. Then, in the nested loop, for each flight (f, t, p), we update the dist of the destination t if a cheaper price is found by considering the flight from f to t with price p. With m being the number of flights, this nested loop has a time complexity of O(m) per iteration of the outer loop.

Putting these together, the total time complexity of the algorithm becomes the combined complexity of the loop executed k+1 times and the nested iteration over all the flights, resulting in O((k+1) * (n + m)) = O(k * (n + m)).

In the worst case, we might have to consider every flight for every iteration, so the worst-case time complexity is O(k * m) when m is significantly larger than n.

Space Complexity

The space complexity of the algorithm is determined by:

1. The dist array, which consumes O(n) space.

2. The backup array, which also consumes O(n) space and is created anew in each iteration of the loop but does not add to the space complexity asymptotically as only one extra array exists at any given time.

Thus, the total space complexity is O(n), due to the space required for the dist and backup arrays to store the cost of reaching each node.

Learn more about how to find time and space complexity quickly using problem constraints.