算法複雜度這件事
這篇文章覆蓋了計算機科學裏面常見算法的時間和空間的大 OBig-O 複雜度。我以前在參加面試前,常常須要花費不少時間從互聯網上查找各類搜索和排序算法的優劣,以便我在面試時不會被問住。最近這幾年,我面試了幾家硅谷的初創企業和一些更大一些的公司,如 Yahoo、eBay、LinkedIn 和 Google,每次我都須要準備這個,我就在問本身,「爲何沒有人建立一個漂亮的大 O 速查表呢?」因此,爲了節省你們的時間,我就建立了這個,但願你喜歡!面試
--- Eric 算法
圖例
數據結構操做
數據結構 |
時間複雜度 |
空間複雜度 |
|
平均 |
最差 |
最差 |
|
訪問 |
搜索 |
插入 |
刪除 |
訪問 |
搜索 |
插入 |
刪除 |
|
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) |
數組排序算法
圖操做
節點 / 邊界管理 |
存儲 |
增長頂點 |
增長邊界 |
移除頂點 |
移除邊界 |
查詢 |
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 複雜度圖表
Big O Complexity Graphapi