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 OBig-O 复杂度。我以前在参加面试前,常常须要花费不少时间从互联网上查找各类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,每次我都须要准备这个,我就在问本身,“为何没有人建立一个漂亮的大 O 速查表呢?”因此,为了节省你们的时间,我就建立了这个,但愿你喜欢!面试
--- Eric 算法
图例
数据结构操做
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 |
|
Array |
O(1) |
O(n) |
O(n) |
O(n) |
O(1) |
O(n) |
O(n) |
O(n) |
O(n) |
Stack |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
Singly-Linked List |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
Doubly-Linked List |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
O(n) |
O(1) |
O(1) |
O(n) |
Skip List |
O(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 Tree |
O(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-Tree |
O(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 Tree |
O(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 Tree |
O(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
图操做
节点 / 边界管理 |
存储 |
增长顶点 |
增长边界 |
移除顶点 |
移除边界 |
查询 |
Adjacency list |
O(|V|+|E|) |
O(1) |
O(1) |
O(|V| + |E|) |
O(|E|) |
O(|V|) |
Incidence list |
O(|V|+|E|) |
O(1) |
O(1) |
O(|E|) |
O(|E|) |
O(|E|) |
Adjacency matrix |
O(|V|^2) |
O(|V|^2) |
O(1) |
O(|V|^2) |
O(1) |
O(1) |
Incidence matrix |
O(|V| ⋅ |E|) |
O(|V| ⋅ |E|) |
O(|V| ⋅ |E|) |
O(|V| ⋅ |E|) |
O(|V| ⋅ |E|) |
O(|E|) |
堆操做
大 O 复杂度图表

Big O Complexity Graphapi