Python 算法教程（99）：图算法与高级应用

图算法详解

图论是高级算法的核心。

DFS 深度优先遍历

def dfs(graph, start, visited=None):
    if visited is None:
        visited = set()
    visited.add(start)
    for neighbor in graph[start]:
        if neighbor not in visited:
            dfs(graph, neighbor, visited)
    return visited

BFS 广度优先遍历

from collections import deque

def bfs(graph, start):
    visited = {start}
    queue = deque([start])
    while queue:
        node = queue.popleft()
        for neighbor in graph[node]:
            if neighbor not in visited:
                visited.add(neighbor)
                queue.append(neighbor)
    return visited

Dijkstra 最短路径

import heapq

def dijkstra(graph, start):
    distances = {node: float('inf') for node in graph}
    distances[start] = 0
    pq = [(0, start)]
    while pq:
        curr_dist, curr_node = heapq.heappop(pq)
        if curr_dist > distances[curr_node]:
            continue
        for neighbor, weight in graph[curr_node].items():
            distance = curr_dist + weight
            if distance < distances[neighbor]:
                distances[neighbor] = distance
                heapq.heappush(pq, (distance, neighbor))
    return distances

回溯算法 - 全排列

def permute(nums):
    result = []
    def backtrack(path, remaining):
        if not remaining:
            result.append(path[:])
            return
        for i in range(len(remaining)):
            path.append(remaining[i])
            backtrack(path, remaining[:i] + remaining[i+1:])
            path.pop()
    backtrack([], nums)
    return result

图论 + 回溯，面试高频考点！

cnbugsai

打赏 海报 链接