算法复杂度速查表
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算法复杂度这件事
这篇文章覆盖了计算机科学里面常见算法的时间和空间的大 O 复杂度。我之前在参加面试前，经常需要花费很多时间从互联网上查找各种搜索和排序算法的优劣，以便我在面试时不会被问住。最近这几年，我面试了几家硅谷的初创企业和一些更大一些的公司，如 Yahoo、eBay、LinkedIn 和 Google，每次我都需要准备这个，我就在问自己，“为什么没有人创建一个漂亮的大 O 速查表呢？”所以，为了节省大家的时间，我就创建了这个，希望你喜欢！
--- Eric
图例
绝佳不错一般不佳糟糕数据结构操作
数据结构 | 时间复杂度 | 空间复杂度 | | 平均 | 最差 | 最差 | | 访问 | 搜索 | 插入 | 删除 | 访问 | 搜索 | 插入 | 删除 | | ArrayO(1)O(n)O(n)O(n)O(1)O(n)O(n)O(n)O(n)StackO(n)O(n)O(1)O(1)O(n)O(n)O(1)O(1)O(n)Singly-Linked ListO(n)O(n)O(1)O(1)O(n)O(n)O(1)O(1)O(n)Doubly-Linked ListO(n)O(n)O(1)O(1)O(n)O(n)O(1)O(1)O(n)Skip ListO(log(n))O(log(n))O(log(n))O(log(n))O(n)O(n)O(n)O(n)O(n log(n))Hash Table-O(1)O(1)O(1)-O(n)O(n)O(n)O(n)Binary Search TreeO(log(n))O(log(n))O(log(n))O(log(n))O(n)O(n)O(n)O(n)O(n)Cartesian Tree-O(log(n))O(log(n))O(log(n))-O(n)O(n)O(n)O(n)B-TreeO(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(n)Red-Black TreeO(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(n)Splay Tree-O(log(n))O(log(n))O(log(n))-O(log(n))O(log(n))O(log(n))O(n)AVL TreeO(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(log(n))O(n)数组排序算法
算法 | 时间复杂度 | 空间复杂度 | | 最佳 | 平均 | 最差 | 最差 | QuicksortO(n log(n))O(n log(n))O(n^2)O(log(n))MergesortO(n log(n))O(n log(n))O(n log(n))O(n)TimsortO(n)O(n log(n))O(n log(n))O(n)HeapsortO(n log(n))O(n log(n))O(n log(n))O(1)Bubble SortO(n)O(n^2)O(n^2)O(1)Insertion SortO(n)O(n^2)O(n^2)O(1)Selection SortO(n^2)O(n^2)O(n^2)O(1)Shell SortO(n)O((nlog(n))^2)O((nlog(n))^2)O(1)Bucket SortO(n+k)O(n+k)O(n^2)O(n)Radix SortO(nk)O(nk)O(nk)O(n+k)图操作
节点 / 边界管理 | 存储 | 增加顶点 | 增加边界 | 移除顶点 | 移除边界 | 查询 | Adjacency listO(|V|+|E|)O(1)O(1)O(|V| + |E|)O(|E|)O(|V|)Incidence listO(|V|+|E|)O(1)O(1)O(|E|)O(|E|)O(|E|)Adjacency matrixO(|V|^2)O(|V|^2)O(1)O(|V|^2)O(1)O(1)Incidence matrixO(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|E|)堆操作
类型 | 时间复杂度 | | Heapify | 查找最大值 | 分离最大值 | 提升键 | 插入 | 删除 | 合并 | Linked List (sorted)-O(1)O(1)O(n)O(n)O(1)O(m+n)Linked List (unsorted)-O(n)O(n)O(1)O(1)O(1)O(1)Binary HeapO(n)O(1)O(log(n))O(log(n))O(log(n))O(log(n))O(m+n)Binomial Heap-O(1)O(log(n))O(log(n))O(1)O(log(n))O(log(n))Fibonacci Heap-O(1)O(log(n))O(1)O(1)O(log(n))O(1)大 O 复杂度图表
Big O Complexity Graph
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原文链接：https://linux.cn/article-7480-1.html
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