当然可以，我会分别给出 Python 实现的 Dijkstra 算法和 Floyd-Warshall 动态规划算法的示例。 首先，让我们看 Dijkstra 算法： ```python import heapq def dijkstra(graph, start): distances = {node: float('infinity') for node in graph} distances[start] = 0 queue = [(0, start)] while queue: current_distance, current_node = heapq.heappop(queue) if current_distance > distances[current_node]: continue for neighbor, weight in graph[current_node].items(): distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(queue, (distance, neighbor)) return distances # 示例图 graph = { 'A': {'B': 1, 'C': 4}, 'B': {'A': 1, 'C': 2, 'D': 5}, 'C': {'A': 4, 'B': 2, 'D': 1}, 'D': {'B': 5, 'C': 1} } start_node = 'A' shortest_distances = dijkstra(graph, start_node) print(f"从 {start_node} 到其他节点的最短距离：{shortest_distances}") ``` 接下来是 Floyd-Warshall 动态规划算法（适用于所有节点对之间的最短路径查找）： ```python def floyd_warshall(graph): n = len(graph) dist_matrix = [[graph[u][v] if u != v else 0 for v in range(n)] for u in range(n)] for k in range(n): for i in range(n): for j in range(n): if dist_matrix[i][j] > dist_matrix[i][k] + dist_matrix[k][j]: dist_matrix[i][j] = dist_matrix[i][k] + dist_matrix[k][j] return dist_matrix # 示例图同上 all_pairs_shortest_paths = floyd_warshall(graph) print(f"所有节点对之间的最短路径矩阵：\n", all_pairs_shortest_paths) ```