为了避免最坏情况的出现，在正权图上应使用效率更高的Dijkstra算法

In order to avoid the worst case, the more efficient Dijkstra algorithm should be used on the positive weight graph. If a given graph has negative weight edges, algorithms like Dijkstra's algorithm will be useless, and the SPFA algorithm will come in handy. In short, when there is no negative weight loop in the weighted directed graph D, the shortest path must exist. Use array d to record the shortest path estimate for each node, and use adjacency table to store graph D. The method we adopt is the dynamic approximation method: set up a first-in first-out queue to save the node to be optimized, and take out the first node u of the team every time during optimization, and use the current shortest path estimation value of u to leave u The pointed node v performs a relaxation operation. If the estimated shortest path of point v is adjusted, and point v is not in the current queue, point v is put at the end of the queue. In this way, nodes are continuously taken out of the queue to perform slack operations until the queue is empty.

To avoid the worst-case scenario, the more efficient Dijkstra algorithm should be used on the positive power map. If a given graph has a negative edge, algorithms such as The Dijkstra algorithm will be useless, and the SPFA algorithm will come in handy. In short, the shortest path must exist when the weighted directional graph D does not have a negative loop. Record the shortest path estimate for each node with array d, and store Figure D with an adjacent table. We take the method of dynamic approximation: set up a first-in-first-out queue to save the node to be optimized, optimize each time to take out the team head node u, and use the current shortest path estimate of u point to leave the u point point point v to relax the operation, if the shortest path estimate of the v point has been adjusted, and v point is not in the current queue, the v point will be placed in the end of the team. This keeps taking nodes out of the queue for relaxation until the queue is empty.

In order to avoid the worst case, Dijkstra algorithm with higher efficiency should be used in the positive weight graph. If a given graph has negative weight edges, algorithms such as Dijkstra algorithm will not be used, and SPFA algorithm will be used. In short, when the weighted digraph D does not have a negative weighted loop, the shortest path must exist. Array D is used to record the shortest path estimation of each node, and adjacency table is used to store graph D. The method we adopt is dynamic approach: set up a first in first out queue to save the nodes to be optimized, take out the first node U of the queue every time during optimization, and use the current shortest path estimation value of u point to relax the node V that points away from u point. If the shortest path estimation value of V point is adjusted and V point is not in the current queue, put V point into the end of the queue. In this way, nodes are taken out of the queue to relax until the queue is empty.