本文介绍了索引,插入和从通用数据结构中删除的时间复杂度是多少?的处理方法,对大家解决问题具有一定的参考价值,需要的朋友们下面随着小编来一起学习吧!

问题描述

没有大的O标记可用于对最常见的数据结构(包括数组,链表,哈希表等)进行操作.

There is no summary available of the big O notation for operations on the most common data structures including arrays, linked lists, hash tables etc.

推荐答案

现在可以在Wikipedia上获得有关此主题的信息:搜索数据结构

Information on this topic is now available on Wikipedia at: Search data structure

+----------------------+----------+------------+----------+--------------+
|                      |  Insert  |   Delete   |  Search  | Space Usage  |
+----------------------+----------+------------+----------+--------------+
| Unsorted array       | O(1)     | O(1)       | O(n)     | O(n)         |
| Value-indexed array  | O(1)     | O(1)       | O(1)     | O(n)         |
| Sorted array         | O(n)     | O(n)       | O(log n) | O(n)         |
| Unsorted linked list | O(1)*    | O(1)*      | O(n)     | O(n)         |
| Sorted linked list   | O(n)*    | O(1)*      | O(n)     | O(n)         |
| Balanced binary tree | O(log n) | O(log n)   | O(log n) | O(n)         |
| Heap                 | O(log n) | O(log n)** | O(n)     | O(n)         |
| Hash table           | O(1)     | O(1)       | O(1)     | O(n)         |
+----------------------+----------+------------+----------+--------------+

 * The cost to add or delete an element into a known location in the list 
   (i.e. if you have an iterator to the location) is O(1). If you don't 
   know the location, then you need to traverse the list to the location
   of deletion/insertion, which takes O(n) time. 

** The deletion cost is O(log n) for the minimum or maximum, O(n) for an
   arbitrary element.

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09-27 11:26