xmengnet/hello-algo
1
0
Fork 0
mirror of https://github.com/krahets/hello-algo.git synced 2024-12-26 01:16:28 +08:00
Code Issues Projects Releases Packages Wiki Activity Actions

build

Browse source
This commit is contained in:
krahets 2024-04-01 05:25:50 +08:00
parent c23e576da4
commit ff7d74401e
28 changed files with 36 additions and 124 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

4
docs/chapter_appendix/index.md
View file

@ -5,12 +5,8 @@ icon: material/help-circle-outline
# 第 16 章   附录
<div class="center-table" markdown>
![附录](../assets/covers/chapter_appendix.jpg){ class="cover-image" }
</div>
## 本章内容
- [16.1 &nbsp; 编程环境安装](https://www.hello-algo.com/chapter_appendix/installation/)

8
docs/chapter_array_and_linkedlist/linked_list.md
View file

@ -184,8 +184,8 @@ comments: true
attr_accessor :val # 节点值
attr_accessor :next # 指向下一节点的引用
def initialize(val=nil, next_node=nil)
@val = val || 0
def initialize(val=0, next_node=nil)
@val = val
@next = next_node
end
end
@ -1511,8 +1511,8 @@ comments: true
attr_accessor :next # 指向后继节点的引用
attr_accessor :prev # 指向前驱节点的引用
def initialize(val=nil, next_node=nil, prev_node=nil)
@val = val || 0
def initialize(val=0, next_node=nil, prev_node=nil)
@val = val
@next = next_node
@prev = prev_node
end

16
docs/chapter_array_and_linkedlist/list.md
View file

@ -287,9 +287,9 @@ comments: true
```ruby title="list.rb"
# 访问元素
num = nums[1]
num = nums[1] # 访问索引 1 处的元素
# 更新元素
nums[1] = 0
nums[1] = 0 # 将索引 1 处的元素更新为 0
```
=== "Zig"
@ -299,7 +299,7 @@ comments: true
var num = nums.items[1]; // 访问索引 1 处的元素
// 更新元素
nums.items[1] = 0; // 将索引 1 处的元素更新为 0
nums.items[1] = 0; // 将索引 1 处的元素更新为 0
```
??? pythontutor "可视化运行"
@ -551,10 +551,10 @@ comments: true
nums << 4
# 在中间插入元素
nums.insert 3, 6
nums.insert 3, 6 # 在索引 3 处插入数字 6
# 删除元素
nums.delete_at 3
nums.delete_at 3 # 删除索引 3 处的元素
```
=== "Zig"
@ -718,7 +718,7 @@ comments: true
for (var i = 0; i < nums.length; i++) {
count += nums[i];
}
/* 直接遍历列表元素 */
count = 0;
for (var num in nums) {
@ -972,7 +972,7 @@ comments: true
=== "JS"
```javascript title="list.js"
/* 排序列表 */
/* 排序列表 */
nums.sort((a, b) => a - b); // 排序后，列表元素从小到大排列
```
@ -1014,7 +1014,7 @@ comments: true
```ruby title="list.rb"
# 排序列表
nums = nums.sort { |a, b| a <=> b }
nums = nums.sort { |a, b| a <=> b } # 排序后，列表元素从小到大排列
```
=== "Zig"

4
docs/chapter_backtracking/index.md
View file

@ -5,12 +5,8 @@ icon: material/map-marker-path
# 第 13 章 &nbsp; 回溯
<div class="center-table" markdown>
![回溯](../assets/covers/chapter_backtracking.jpg){ class="cover-image" }
</div>
!!! abstract
我们如同迷宫中的探索者，在前进的道路上可能会遇到困难。

4
docs/chapter_computational_complexity/index.md
View file

@ -5,12 +5,8 @@ icon: material/timer-sand
# 第 2 章 &nbsp; 复杂度分析
<div class="center-table" markdown>
![复杂度分析](../assets/covers/chapter_complexity_analysis.jpg){ class="cover-image" }
</div>
!!! abstract
复杂度分析犹如浩瀚的算法宇宙中的时空向导。

10
docs/chapter_computational_complexity/space_complexity.md
View file

@ -798,7 +798,7 @@ $$
<p align="center"> 图 2-16 &nbsp; 常见的空间复杂度类型 </p>
### 1. &nbsp; 常数阶 $O(1)$ {data-toc-label="常数阶"}
### 1. &nbsp; 常数阶 $O(1)$ {data-toc-label="1. &nbsp; 常数阶"}
常数阶常见于数量与输入数据大小 $n$ 无关的常量、变量、对象。
@ -1178,7 +1178,7 @@ $$
<div style="height: 549px; width: 100%;"><iframe class="pythontutor-iframe" src="https://pythontutor.com/iframe-embed.html#code=class%20ListNode%3A%0A%20%20%20%20%22%22%22%E9%93%BE%E8%A1%A8%E8%8A%82%E7%82%B9%E7%B1%BB%22%22%22%0A%20%20%20%20def%20__init__%28self,%20val%3A%20int%29%3A%0A%20%20%20%20%20%20%20%20self.val%3A%20int%20%3D%20val%20%20%23%20%E8%8A%82%E7%82%B9%E5%80%BC%0A%20%20%20%20%20%20%20%20self.next%3A%20ListNode%20%7C%20None%20%3D%20None%20%20%23%20%E5%90%8E%E7%BB%A7%E8%8A%82%E7%82%B9%E5%BC%95%E7%94%A8%0A%0Adef%20function%28%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%87%BD%E6%95%B0%22%22%22%0A%20%20%20%20%23%20%E6%89%A7%E8%A1%8C%E6%9F%90%E4%BA%9B%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%200%0A%0Adef%20constant%28n%3A%20int%29%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20%23%20%E5%B8%B8%E9%87%8F%E3%80%81%E5%8F%98%E9%87%8F%E3%80%81%E5%AF%B9%E8%B1%A1%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20a%20%3D%200%0A%20%20%20%20nums%20%3D%20%5B0%5D%20*%2010%0A%20%20%20%20node%20%3D%20ListNode%280%29%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%8F%98%E9%87%8F%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20c%20%3D%200%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%87%BD%E6%95%B0%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20function%28%29%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%205%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20%23%20%E5%B8%B8%E6%95%B0%E9%98%B6%0A%20%20%20%20constant%28n%29&codeDivHeight=472&codeDivWidth=350&cumulative=false&curInstr=6&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false"> </iframe></div>
<div style="margin-top: 5px;"><a href="https://pythontutor.com/iframe-embed.html#code=class%20ListNode%3A%0A%20%20%20%20%22%22%22%E9%93%BE%E8%A1%A8%E8%8A%82%E7%82%B9%E7%B1%BB%22%22%22%0A%20%20%20%20def%20__init__%28self,%20val%3A%20int%29%3A%0A%20%20%20%20%20%20%20%20self.val%3A%20int%20%3D%20val%20%20%23%20%E8%8A%82%E7%82%B9%E5%80%BC%0A%20%20%20%20%20%20%20%20self.next%3A%20ListNode%20%7C%20None%20%3D%20None%20%20%23%20%E5%90%8E%E7%BB%A7%E8%8A%82%E7%82%B9%E5%BC%95%E7%94%A8%0A%0Adef%20function%28%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%87%BD%E6%95%B0%22%22%22%0A%20%20%20%20%23%20%E6%89%A7%E8%A1%8C%E6%9F%90%E4%BA%9B%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%200%0A%0Adef%20constant%28n%3A%20int%29%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20%23%20%E5%B8%B8%E9%87%8F%E3%80%81%E5%8F%98%E9%87%8F%E3%80%81%E5%AF%B9%E8%B1%A1%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20a%20%3D%200%0A%20%20%20%20nums%20%3D%20%5B0%5D%20*%2010%0A%20%20%20%20node%20%3D%20ListNode%280%29%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%8F%98%E9%87%8F%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20c%20%3D%200%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%87%BD%E6%95%B0%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20function%28%29%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%205%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20%23%20%E5%B8%B8%E6%95%B0%E9%98%B6%0A%20%20%20%20constant%28n%29&codeDivHeight=800&codeDivWidth=600&cumulative=false&curInstr=6&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false" target="_blank" rel="noopener noreferrer">全屏观看 ></a></div>
### 2. &nbsp; 线性阶 $O(n)$ {data-toc-label="线性阶"}
### 2. &nbsp; 线性阶 $O(n)$ {data-toc-label="2. &nbsp; 线性阶"}
线性阶常见于元素数量与 $n$ 成正比的数组、链表、栈、队列等：
@ -1643,7 +1643,7 @@ $$
<p align="center"> 图 2-17 &nbsp; 递归函数产生的线性阶空间复杂度 </p>
### 3. &nbsp; 平方阶 $O(n^2)$ {data-toc-label="平方阶"}
### 3. &nbsp; 平方阶 $O(n^2)$ {data-toc-label="3. &nbsp; 平方阶"}
平方阶常见于矩阵和图，元素数量与 $n$ 成平方关系：
@ -2079,7 +2079,7 @@ $$
<p align="center"> 图 2-18 &nbsp; 递归函数产生的平方阶空间复杂度 </p>
### 4. &nbsp; 指数阶 $O(2^n)$ {data-toc-label="指数阶"}
### 4. &nbsp; 指数阶 $O(2^n)$ {data-toc-label="4. &nbsp; 指数阶"}
指数阶常见于二叉树。观察图 2-19 ，层数为 $n$ 的“满二叉树”的节点数量为 $2^n - 1$ ，占用 $O(2^n)$ 空间：
@ -2279,7 +2279,7 @@ $$
<p align="center"> 图 2-19 &nbsp; 满二叉树产生的指数阶空间复杂度 </p>
### 5. &nbsp; 对数阶 $O(\log n)$ {data-toc-label="对数阶"}
### 5. &nbsp; 对数阶 $O(\log n)$ {data-toc-label="5. &nbsp; 对数阶"}
对数阶常见于分治算法。例如归并排序，输入长度为 $n$ 的数组，每轮递归将数组从中点处划分为两半，形成高度为 $\log n$ 的递归树，使用 $O(\log n)$ 栈帧空间。

14
docs/chapter_computational_complexity/time_complexity.md
View file

@ -1047,7 +1047,7 @@ $$
<p align="center"> 图 2-9 &nbsp; 常见的时间复杂度类型 </p>
### 1. &nbsp; 常数阶 $O(1)$ {data-toc-label="常数阶"}
### 1. &nbsp; 常数阶 $O(1)$ {data-toc-label="1. &nbsp; 常数阶"}
常数阶的操作数量与输入数据大小 $n$ 无关，即不随着 $n$ 的变化而变化。
@ -1240,7 +1240,7 @@ $$
<div style="height: 459px; width: 100%;"><iframe class="pythontutor-iframe" src="https://pythontutor.com/iframe-embed.html#code=def%20constant%28n%3A%20int%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20count%20%3D%200%0A%20%20%20%20size%20%3D%2010%0A%20%20%20%20for%20_%20in%20range%28size%29%3A%0A%20%20%20%20%20%20%20%20count%20%2B%3D%201%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20count%20%3D%20constant%28n%29%0A%20%20%20%20print%28%22%E5%B8%B8%E6%95%B0%E9%98%B6%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=472&codeDivWidth=350&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false"> </iframe></div>
<div style="margin-top: 5px;"><a href="https://pythontutor.com/iframe-embed.html#code=def%20constant%28n%3A%20int%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20count%20%3D%200%0A%20%20%20%20size%20%3D%2010%0A%20%20%20%20for%20_%20in%20range%28size%29%3A%0A%20%20%20%20%20%20%20%20count%20%2B%3D%201%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20count%20%3D%20constant%28n%29%0A%20%20%20%20print%28%22%E5%B8%B8%E6%95%B0%E9%98%B6%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=800&codeDivWidth=600&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false" target="_blank" rel="noopener noreferrer">全屏观看 ></a></div>
### 2. &nbsp; 线性阶 $O(n)$ {data-toc-label="线性阶"}
### 2. &nbsp; 线性阶 $O(n)$ {data-toc-label="2. &nbsp; 线性阶"}
线性阶的操作数量相对于输入数据大小 $n$ 以线性级别增长。线性阶通常出现在单层循环中：
@ -1611,7 +1611,7 @@ $$
值得注意的是，**输入数据大小 $n$ 需根据输入数据的类型来具体确定**。比如在第一个示例中，变量 $n$ 为输入数据大小；在第二个示例中，数组长度 $n$ 为数据大小。
### 3. &nbsp; 平方阶 $O(n^2)$ {data-toc-label="平方阶"}
### 3. &nbsp; 平方阶 $O(n^2)$ {data-toc-label="3. &nbsp; 平方阶"}
平方阶的操作数量相对于输入数据大小 $n$ 以平方级别增长。平方阶通常出现在嵌套循环中，外层循环和内层循环的时间复杂度都为 $O(n)$ ，因此总体的时间复杂度为 $O(n^2)$ ：
@ -2146,7 +2146,7 @@ $$
<div style="height: 549px; width: 100%;"><iframe class="pythontutor-iframe" src="https://pythontutor.com/iframe-embed.html#code=def%20bubble_sort%28nums%3A%20list%5Bint%5D%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%22%22%22%0A%20%20%20%20count%20%3D%200%20%20%23%20%E8%AE%A1%E6%95%B0%E5%99%A8%0A%20%20%20%20%23%20%E5%A4%96%E5%BE%AA%E7%8E%AF%EF%BC%9A%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%E4%B8%BA%20%5B0,%20i%5D%0A%20%20%20%20for%20i%20in%20range%28len%28nums%29%20-%201,%200,%20-1%29%3A%0A%20%20%20%20%20%20%20%20%23%20%E5%86%85%E5%BE%AA%E7%8E%AF%EF%BC%9A%E5%B0%86%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%20%5B0,%20i%5D%20%E4%B8%AD%E7%9A%84%E6%9C%80%E5%A4%A7%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E8%87%B3%E8%AF%A5%E5%8C%BA%E9%97%B4%E7%9A%84%E6%9C%80%E5%8F%B3%E7%AB%AF%0A%20%20%20%20%20%20%20%20for%20j%20in%20range%28i%29%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20nums%5Bj%5D%20%3E%20nums%5Bj%20%2B%201%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E4%BA%A4%E6%8D%A2%20nums%5Bj%5D%20%E4%B8%8E%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tmp%20%3D%20nums%5Bj%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%5D%20%3D%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%20%2B%201%5D%20%3D%20tmp%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20count%20%2B%3D%203%20%20%23%20%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E5%8C%85%E5%90%AB%203%20%E4%B8%AA%E5%8D%95%E5%85%83%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20nums%20%3D%20%5Bi%20for%20i%20in%20range%28n,%200,%20-1%29%5D%20%20%23%20%5Bn,%20n-1,%20...,%202,%201%5D%0A%20%20%20%20count%20%3D%20bubble_sort%28nums%29%0A%20%20%20%20print%28%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=472&codeDivWidth=350&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false"> </iframe></div>
<div style="margin-top: 5px;"><a href="https://pythontutor.com/iframe-embed.html#code=def%20bubble_sort%28nums%3A%20list%5Bint%5D%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%22%22%22%0A%20%20%20%20count%20%3D%200%20%20%23%20%E8%AE%A1%E6%95%B0%E5%99%A8%0A%20%20%20%20%23%20%E5%A4%96%E5%BE%AA%E7%8E%AF%EF%BC%9A%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%E4%B8%BA%20%5B0,%20i%5D%0A%20%20%20%20for%20i%20in%20range%28len%28nums%29%20-%201,%200,%20-1%29%3A%0A%20%20%20%20%20%20%20%20%23%20%E5%86%85%E5%BE%AA%E7%8E%AF%EF%BC%9A%E5%B0%86%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%20%5B0,%20i%5D%20%E4%B8%AD%E7%9A%84%E6%9C%80%E5%A4%A7%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E8%87%B3%E8%AF%A5%E5%8C%BA%E9%97%B4%E7%9A%84%E6%9C%80%E5%8F%B3%E7%AB%AF%0A%20%20%20%20%20%20%20%20for%20j%20in%20range%28i%29%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20nums%5Bj%5D%20%3E%20nums%5Bj%20%2B%201%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E4%BA%A4%E6%8D%A2%20nums%5Bj%5D%20%E4%B8%8E%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tmp%20%3D%20nums%5Bj%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%5D%20%3D%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%20%2B%201%5D%20%3D%20tmp%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20count%20%2B%3D%203%20%20%23%20%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E5%8C%85%E5%90%AB%203%20%E4%B8%AA%E5%8D%95%E5%85%83%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20nums%20%3D%20%5Bi%20for%20i%20in%20range%28n,%200,%20-1%29%5D%20%20%23%20%5Bn,%20n-1,%20...,%202,%201%5D%0A%20%20%20%20count%20%3D%20bubble_sort%28nums%29%0A%20%20%20%20print%28%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=800&codeDivWidth=600&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false" target="_blank" rel="noopener noreferrer">全屏观看 ></a></div>
### 4. &nbsp; 指数阶 $O(2^n)$ {data-toc-label="指数阶"}
### 4. &nbsp; 指数阶 $O(2^n)$ {data-toc-label="4. &nbsp; 指数阶"}
生物学的“细胞分裂”是指数阶增长的典型例子：初始状态为 $1$ 个细胞，分裂一轮后变为 $2$ 个，分裂两轮后变为 $4$ 个，以此类推，分裂 $n$ 轮后有 $2^n$ 个细胞。
@ -2564,7 +2564,7 @@ $$
指数阶增长非常迅速，在穷举法（暴力搜索、回溯等）中比较常见。对于数据规模较大的问题，指数阶是不可接受的，通常需要使用动态规划或贪心算法等来解决。
### 5. &nbsp; 对数阶 $O(\log n)$ {data-toc-label="对数阶"}
### 5. &nbsp; 对数阶 $O(\log n)$ {data-toc-label="5. &nbsp; 对数阶"}
与指数阶相反，对数阶反映了“每轮缩减到一半”的情况。设输入数据大小为 $n$ ，由于每轮缩减到一半，因此循环次数是 $\log_2 n$ ，即 $2^n$ 的反函数。
@ -2934,7 +2934,7 @@ $$
也就是说，底数 $m$ 可以在不影响复杂度的前提下转换。因此我们通常会省略底数 $m$ ，将对数阶直接记为 $O(\log n)$ 。
### 6. &nbsp; 线性对数阶 $O(n \log n)$ {data-toc-label="线性对数阶"}
### 6. &nbsp; 线性对数阶 $O(n \log n)$ {data-toc-label="6. &nbsp; 线性对数阶"}
线性对数阶常出现于嵌套循环中，两层循环的时间复杂度分别为 $O(\log n)$ 和 $O(n)$ 。相关代码如下：
@ -3149,7 +3149,7 @@ $$
主流排序算法的时间复杂度通常为 $O(n \log n)$ ，例如快速排序、归并排序、堆排序等。
### 7. &nbsp; 阶乘阶 $O(n!)$ {data-toc-label="阶乘阶"}
### 7. &nbsp; 阶乘阶 $O(n!)$ {data-toc-label="7. &nbsp; 阶乘阶"}
阶乘阶对应数学上的“全排列”问题。给定 $n$ 个互不重复的元素，求其所有可能的排列方案，方案数量为：

4
docs/chapter_data_structure/index.md
View file

@ -5,12 +5,8 @@ icon: material/shape-outline
# 第 3 章 &nbsp; 数据结构
<div class="center-table" markdown>
![数据结构](../assets/covers/chapter_data_structure.jpg){ class="cover-image" }
</div>
!!! abstract
数据结构如同一副稳固而多样的框架。

4
docs/chapter_divide_and_conquer/index.md
View file

@ -5,12 +5,8 @@ icon: material/set-split
# 第 12 章 &nbsp; 分治
<div class="center-table" markdown>
![分治](../assets/covers/chapter_divide_and_conquer.jpg){ class="cover-image" }
</div>
!!! abstract
难题被逐层拆解，每一次的拆解都使它变得更为简单。

4
docs/chapter_dynamic_programming/index.md
View file

@ -5,12 +5,8 @@ icon: material/table-pivot
# 第 14 章 &nbsp; 动态规划
<div class="center-table" markdown>
![动态规划](../assets/covers/chapter_dynamic_programming.jpg){ class="cover-image" }
</div>
!!! abstract
小溪汇入河流，江河汇入大海。

4
docs/chapter_graph/index.md
View file

@ -5,12 +5,8 @@ icon: material/graphql
# 第 9 章 &nbsp; 图
<div class="center-table" markdown>
![图](../assets/covers/chapter_graph.jpg){ class="cover-image" }
</div>
!!! abstract
在生命旅途中，我们就像是一个个节点，被无数看不见的边相连。

4
docs/chapter_greedy/index.md
View file

@ -5,12 +5,8 @@ icon: material/head-heart-outline
# 第 15 章 &nbsp; 贪心
<div class="center-table" markdown>
![贪心](../assets/covers/chapter_greedy.jpg){ class="cover-image" }
</div>
!!! abstract
向日葵朝着太阳转动，时刻追求自身成长的最大可能。

4
docs/chapter_hashing/index.md
View file

@ -5,12 +5,8 @@ icon: material/table-search
# 第 6 章 &nbsp; 哈希表
<div class="center-table" markdown>
![哈希表](../assets/covers/chapter_hashing.jpg){ class="cover-image" }
</div>
!!! abstract
在计算机世界中，哈希表如同一位聪慧的图书管理员。

4
docs/chapter_heap/index.md
View file

@ -5,12 +5,8 @@ icon: material/family-tree
# 第 8 章 &nbsp; 堆
<div class="center-table" markdown>
![堆](../assets/covers/chapter_heap.jpg){ class="cover-image" }
</div>
!!! abstract
堆就像是山岳峰峦，层叠起伏、形态各异。

4
docs/chapter_introduction/index.md
View file

@ -5,12 +5,8 @@ icon: material/calculator-variant-outline
# 第 1 章 &nbsp; 初识算法
<div class="center-table" markdown>
![初识算法](../assets/covers/chapter_introduction.jpg){ class="cover-image" }
</div>
!!! abstract
一位少女翩翩起舞，与数据交织在一起，裙摆上飘扬着算法的旋律。

4
docs/chapter_preface/index.md
View file

@ -5,12 +5,8 @@ icon: material/book-open-outline
# 第 0 章 &nbsp; 前言
<div class="center-table" markdown>
![前言](../assets/covers/chapter_preface.jpg){ class="cover-image" }
</div>
!!! abstract
算法犹如美妙的交响乐，每一行代码都像韵律般流淌。

4
docs/chapter_searching/index.md
View file

@ -5,12 +5,8 @@ icon: material/text-search
# 第 10 章 &nbsp; 搜索
<div class="center-table" markdown>
![搜索](../assets/covers/chapter_searching.jpg){ class="cover-image" }
</div>
!!! abstract
搜索是一场未知的冒险，我们或许需要走遍神秘空间的每个角落，又或许可以快速锁定目标。

4
docs/chapter_sorting/index.md
View file

@ -5,12 +5,8 @@ icon: material/sort-ascending
# 第 11 章 &nbsp; 排序
<div class="center-table" markdown>
![排序](../assets/covers/chapter_sorting.jpg){ class="cover-image" }
</div>
!!! abstract
排序犹如一把将混乱变为秩序的魔法钥匙，使我们能以更高效的方式理解与处理数据。

4
docs/chapter_stack_and_queue/index.md
View file

@ -5,12 +5,8 @@ icon: material/stack-overflow
# 第 5 章 &nbsp; 栈与队列
<div class="center-table" markdown>
![栈与队列](../assets/covers/chapter_stack_and_queue.jpg){ class="cover-image" }
</div>
!!! abstract
栈如同叠猫猫，而队列就像猫猫排队。

4
docs/chapter_tree/index.md
View file

@ -5,12 +5,8 @@ icon: material/graph-outline
# 第 7 章 &nbsp; 树
<div class="center-table" markdown>
![树](../assets/covers/chapter_tree.jpg){ class="cover-image" }
</div>
!!! abstract
参天大树充满生命力，根深叶茂，分枝扶疏。

4
en/docs/chapter_computational_complexity/index.md
View file

@ -5,12 +5,8 @@ icon: material/timer-sand
# Chapter 2. &nbsp; Complexity Analysis
<div class="center-table" markdown>
![complexity_analysis](../assets/covers/chapter_complexity_analysis.jpg){ class="cover-image" }
</div>
!!! abstract
Complexity analysis is like a space-time navigator in the vast universe of algorithms.

10
en/docs/chapter_computational_complexity/space_complexity.md
View file

@ -744,7 +744,7 @@ $$
<p align="center"> Figure 2-16 &nbsp; Common Types of Space Complexity </p>
### 1. &nbsp; Constant Order $O(1)$ {data-toc-label="Constant Order"}
### 1. &nbsp; Constant Order $O(1)$ {data-toc-label="1. &nbsp; Constant Order"}
Constant order is common in constants, variables, objects that are independent of the size of input data $n$.
@ -1124,7 +1124,7 @@ Note that memory occupied by initializing variables or calling functions in a lo
<div style="height: 549px; width: 100%;"><iframe class="pythontutor-iframe" src="https://pythontutor.com/iframe-embed.html#code=class%20ListNode%3A%0A%20%20%20%20%22%22%22%E9%93%BE%E8%A1%A8%E8%8A%82%E7%82%B9%E7%B1%BB%22%22%22%0A%20%20%20%20def%20__init__%28self,%20val%3A%20int%29%3A%0A%20%20%20%20%20%20%20%20self.val%3A%20int%20%3D%20val%20%20%23%20%E8%8A%82%E7%82%B9%E5%80%BC%0A%20%20%20%20%20%20%20%20self.next%3A%20ListNode%20%7C%20None%20%3D%20None%20%20%23%20%E5%90%8E%E7%BB%A7%E8%8A%82%E7%82%B9%E5%BC%95%E7%94%A8%0A%0Adef%20function%28%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%87%BD%E6%95%B0%22%22%22%0A%20%20%20%20%23%20%E6%89%A7%E8%A1%8C%E6%9F%90%E4%BA%9B%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%200%0A%0Adef%20constant%28n%3A%20int%29%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20%23%20%E5%B8%B8%E9%87%8F%E3%80%81%E5%8F%98%E9%87%8F%E3%80%81%E5%AF%B9%E8%B1%A1%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20a%20%3D%200%0A%20%20%20%20nums%20%3D%20%5B0%5D%20*%2010%0A%20%20%20%20node%20%3D%20ListNode%280%29%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%8F%98%E9%87%8F%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20c%20%3D%200%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%87%BD%E6%95%B0%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20function%28%29%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%205%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20%23%20%E5%B8%B8%E6%95%B0%E9%98%B6%0A%20%20%20%20constant%28n%29&codeDivHeight=472&codeDivWidth=350&cumulative=false&curInstr=6&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false"> </iframe></div>
<div style="margin-top: 5px;"><a href="https://pythontutor.com/iframe-embed.html#code=class%20ListNode%3A%0A%20%20%20%20%22%22%22%E9%93%BE%E8%A1%A8%E8%8A%82%E7%82%B9%E7%B1%BB%22%22%22%0A%20%20%20%20def%20__init__%28self,%20val%3A%20int%29%3A%0A%20%20%20%20%20%20%20%20self.val%3A%20int%20%3D%20val%20%20%23%20%E8%8A%82%E7%82%B9%E5%80%BC%0A%20%20%20%20%20%20%20%20self.next%3A%20ListNode%20%7C%20None%20%3D%20None%20%20%23%20%E5%90%8E%E7%BB%A7%E8%8A%82%E7%82%B9%E5%BC%95%E7%94%A8%0A%0Adef%20function%28%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%87%BD%E6%95%B0%22%22%22%0A%20%20%20%20%23%20%E6%89%A7%E8%A1%8C%E6%9F%90%E4%BA%9B%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%200%0A%0Adef%20constant%28n%3A%20int%29%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20%23%20%E5%B8%B8%E9%87%8F%E3%80%81%E5%8F%98%E9%87%8F%E3%80%81%E5%AF%B9%E8%B1%A1%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20a%20%3D%200%0A%20%20%20%20nums%20%3D%20%5B0%5D%20*%2010%0A%20%20%20%20node%20%3D%20ListNode%280%29%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%8F%98%E9%87%8F%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20c%20%3D%200%0A%20%20%20%20%23%20%E5%BE%AA%E7%8E%AF%E4%B8%AD%E7%9A%84%E5%87%BD%E6%95%B0%E5%8D%A0%E7%94%A8%20O%281%29%20%E7%A9%BA%E9%97%B4%0A%20%20%20%20for%20_%20in%20range%28n%29%3A%0A%20%20%20%20%20%20%20%20function%28%29%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%205%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20%23%20%E5%B8%B8%E6%95%B0%E9%98%B6%0A%20%20%20%20constant%28n%29&codeDivHeight=800&codeDivWidth=600&cumulative=false&curInstr=6&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false" target="_blank" rel="noopener noreferrer">Full Screen ></a></div>
### 2. &nbsp; Linear Order $O(n)$ {data-toc-label="Linear Order"}
### 2. &nbsp; Linear Order $O(n)$ {data-toc-label="2. &nbsp; Linear Order"}
Linear order is common in arrays, linked lists, stacks, queues, etc., where the number of elements is proportional to $n$:
@ -1589,7 +1589,7 @@ As shown below, this function's recursive depth is $n$, meaning there are $n$ in
<p align="center"> Figure 2-17 &nbsp; Recursive Function Generating Linear Order Space Complexity </p>
### 3. &nbsp; Quadratic Order $O(n^2)$ {data-toc-label="Quadratic Order"}
### 3. &nbsp; Quadratic Order $O(n^2)$ {data-toc-label="3. &nbsp; Quadratic Order"}
Quadratic order is common in matrices and graphs, where the number of elements is quadratic to $n$:
@ -2025,7 +2025,7 @@ As shown below, the recursive depth of this function is $n$, and in each recursi
<p align="center"> Figure 2-18 &nbsp; Recursive Function Generating Quadratic Order Space Complexity </p>
### 4. &nbsp; Exponential Order $O(2^n)$ {data-toc-label="Exponential Order"}
### 4. &nbsp; Exponential Order $O(2^n)$ {data-toc-label="4. &nbsp; Exponential Order"}
Exponential order is common in binary trees. Observe the below image, a "full binary tree" with $n$ levels has $2^n - 1$ nodes, occupying $O(2^n)$ space:
@ -2225,7 +2225,7 @@ Exponential order is common in binary trees. Observe the below image, a "full bi
<p align="center"> Figure 2-19 &nbsp; Full Binary Tree Generating Exponential Order Space Complexity </p>
### 5. &nbsp; Logarithmic Order $O(\log n)$ {data-toc-label="Logarithmic Order"}
### 5. &nbsp; Logarithmic Order $O(\log n)$ {data-toc-label="5. &nbsp; Logarithmic Order"}
Logarithmic order is common in divide-and-conquer algorithms. For example, in merge sort, an array of length $n$ is recursively divided in half each round, forming a recursion tree of height $\log n$, using $O(\log n)$ stack frame space.

14
en/docs/chapter_computational_complexity/time_complexity.md
View file

@ -976,7 +976,7 @@ $$
<p align="center"> Figure 2-9 &nbsp; Common Types of Time Complexity </p>
### 1. &nbsp; Constant Order $O(1)$ {data-toc-label="Constant Order"}
### 1. &nbsp; Constant Order $O(1)$ {data-toc-label="1. &nbsp; Constant Order"}
Constant order means the number of operations is independent of the input data size $n$. In the following function, although the number of operations `size` might be large, the time complexity remains $O(1)$ as it's unrelated to $n$:
@ -1167,7 +1167,7 @@ Constant order means the number of operations is independent of the input data s
<div style="height: 459px; width: 100%;"><iframe class="pythontutor-iframe" src="https://pythontutor.com/iframe-embed.html#code=def%20constant%28n%3A%20int%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20count%20%3D%200%0A%20%20%20%20size%20%3D%2010%0A%20%20%20%20for%20_%20in%20range%28size%29%3A%0A%20%20%20%20%20%20%20%20count%20%2B%3D%201%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20count%20%3D%20constant%28n%29%0A%20%20%20%20print%28%22%E5%B8%B8%E6%95%B0%E9%98%B6%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=472&codeDivWidth=350&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false"> </iframe></div>
<div style="margin-top: 5px;"><a href="https://pythontutor.com/iframe-embed.html#code=def%20constant%28n%3A%20int%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B8%B8%E6%95%B0%E9%98%B6%22%22%22%0A%20%20%20%20count%20%3D%200%0A%20%20%20%20size%20%3D%2010%0A%20%20%20%20for%20_%20in%20range%28size%29%3A%0A%20%20%20%20%20%20%20%20count%20%2B%3D%201%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20count%20%3D%20constant%28n%29%0A%20%20%20%20print%28%22%E5%B8%B8%E6%95%B0%E9%98%B6%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=800&codeDivWidth=600&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false" target="_blank" rel="noopener noreferrer">Full Screen ></a></div>
### 2. &nbsp; Linear Order $O(n)$ {data-toc-label="Linear Order"}
### 2. &nbsp; Linear Order $O(n)$ {data-toc-label="2. &nbsp; Linear Order"}
Linear order indicates the number of operations grows linearly with the input data size $n$. Linear order commonly appears in single-loop structures:
@ -1538,7 +1538,7 @@ Operations like array traversal and linked list traversal have a time complexity
It's important to note that **the input data size $n$ should be determined based on the type of input data**. For example, in the first example, $n$ represents the input data size, while in the second example, the length of the array $n$ is the data size.
### 3. &nbsp; Quadratic Order $O(n^2)$ {data-toc-label="Quadratic Order"}
### 3. &nbsp; Quadratic Order $O(n^2)$ {data-toc-label="3. &nbsp; Quadratic Order"}
Quadratic order means the number of operations grows quadratically with the input data size $n$. Quadratic order typically appears in nested loops, where both the outer and inner loops have a time complexity of $O(n)$, resulting in an overall complexity of $O(n^2)$:
@ -2073,7 +2073,7 @@ For instance, in bubble sort, the outer loop runs $n - 1$ times, and the inner l
<div style="height: 549px; width: 100%;"><iframe class="pythontutor-iframe" src="https://pythontutor.com/iframe-embed.html#code=def%20bubble_sort%28nums%3A%20list%5Bint%5D%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%22%22%22%0A%20%20%20%20count%20%3D%200%20%20%23%20%E8%AE%A1%E6%95%B0%E5%99%A8%0A%20%20%20%20%23%20%E5%A4%96%E5%BE%AA%E7%8E%AF%EF%BC%9A%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%E4%B8%BA%20%5B0,%20i%5D%0A%20%20%20%20for%20i%20in%20range%28len%28nums%29%20-%201,%200,%20-1%29%3A%0A%20%20%20%20%20%20%20%20%23%20%E5%86%85%E5%BE%AA%E7%8E%AF%EF%BC%9A%E5%B0%86%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%20%5B0,%20i%5D%20%E4%B8%AD%E7%9A%84%E6%9C%80%E5%A4%A7%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E8%87%B3%E8%AF%A5%E5%8C%BA%E9%97%B4%E7%9A%84%E6%9C%80%E5%8F%B3%E7%AB%AF%0A%20%20%20%20%20%20%20%20for%20j%20in%20range%28i%29%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20nums%5Bj%5D%20%3E%20nums%5Bj%20%2B%201%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E4%BA%A4%E6%8D%A2%20nums%5Bj%5D%20%E4%B8%8E%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tmp%20%3D%20nums%5Bj%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%5D%20%3D%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%20%2B%201%5D%20%3D%20tmp%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20count%20%2B%3D%203%20%20%23%20%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E5%8C%85%E5%90%AB%203%20%E4%B8%AA%E5%8D%95%E5%85%83%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20nums%20%3D%20%5Bi%20for%20i%20in%20range%28n,%200,%20-1%29%5D%20%20%23%20%5Bn,%20n-1,%20...,%202,%201%5D%0A%20%20%20%20count%20%3D%20bubble_sort%28nums%29%0A%20%20%20%20print%28%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=472&codeDivWidth=350&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false"> </iframe></div>
<div style="margin-top: 5px;"><a href="https://pythontutor.com/iframe-embed.html#code=def%20bubble_sort%28nums%3A%20list%5Bint%5D%29%20-%3E%20int%3A%0A%20%20%20%20%22%22%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%22%22%22%0A%20%20%20%20count%20%3D%200%20%20%23%20%E8%AE%A1%E6%95%B0%E5%99%A8%0A%20%20%20%20%23%20%E5%A4%96%E5%BE%AA%E7%8E%AF%EF%BC%9A%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%E4%B8%BA%20%5B0,%20i%5D%0A%20%20%20%20for%20i%20in%20range%28len%28nums%29%20-%201,%200,%20-1%29%3A%0A%20%20%20%20%20%20%20%20%23%20%E5%86%85%E5%BE%AA%E7%8E%AF%EF%BC%9A%E5%B0%86%E6%9C%AA%E6%8E%92%E5%BA%8F%E5%8C%BA%E9%97%B4%20%5B0,%20i%5D%20%E4%B8%AD%E7%9A%84%E6%9C%80%E5%A4%A7%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E8%87%B3%E8%AF%A5%E5%8C%BA%E9%97%B4%E7%9A%84%E6%9C%80%E5%8F%B3%E7%AB%AF%0A%20%20%20%20%20%20%20%20for%20j%20in%20range%28i%29%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20if%20nums%5Bj%5D%20%3E%20nums%5Bj%20%2B%201%5D%3A%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%23%20%E4%BA%A4%E6%8D%A2%20nums%5Bj%5D%20%E4%B8%8E%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20tmp%20%3D%20nums%5Bj%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%5D%20%3D%20nums%5Bj%20%2B%201%5D%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20nums%5Bj%20%2B%201%5D%20%3D%20tmp%0A%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20%20count%20%2B%3D%203%20%20%23%20%E5%85%83%E7%B4%A0%E4%BA%A4%E6%8D%A2%E5%8C%85%E5%90%AB%203%20%E4%B8%AA%E5%8D%95%E5%85%83%E6%93%8D%E4%BD%9C%0A%20%20%20%20return%20count%0A%0A%22%22%22Driver%20Code%22%22%22%0Aif%20__name__%20%3D%3D%20%22__main__%22%3A%0A%20%20%20%20n%20%3D%208%0A%20%20%20%20print%28%22%E8%BE%93%E5%85%A5%E6%95%B0%E6%8D%AE%E5%A4%A7%E5%B0%8F%20n%20%3D%22,%20n%29%0A%0A%20%20%20%20nums%20%3D%20%5Bi%20for%20i%20in%20range%28n,%200,%20-1%29%5D%20%20%23%20%5Bn,%20n-1,%20...,%202,%201%5D%0A%20%20%20%20count%20%3D%20bubble_sort%28nums%29%0A%20%20%20%20print%28%22%E5%B9%B3%E6%96%B9%E9%98%B6%EF%BC%88%E5%86%92%E6%B3%A1%E6%8E%92%E5%BA%8F%EF%BC%89%E7%9A%84%E6%93%8D%E4%BD%9C%E6%95%B0%E9%87%8F%20%3D%22,%20count%29&codeDivHeight=800&codeDivWidth=600&cumulative=false&curInstr=3&heapPrimitives=nevernest&origin=opt-frontend.js&py=311&rawInputLstJSON=%5B%5D&textReferences=false" target="_blank" rel="noopener noreferrer">Full Screen ></a></div>
### 4. &nbsp; Exponential Order $O(2^n)$ {data-toc-label="Exponential Order"}
### 4. &nbsp; Exponential Order $O(2^n)$ {data-toc-label="4. &nbsp; Exponential Order"}
Biological "cell division" is a classic example of exponential order growth: starting with one cell, it becomes two after one division, four after two divisions, and so on, resulting in $2^n$ cells after $n$ divisions.
@ -2491,7 +2491,7 @@ In practice, exponential order often appears in recursive functions. For example
Exponential order growth is extremely rapid and is commonly seen in exhaustive search methods (brute force, backtracking, etc.). For large-scale problems, exponential order is unacceptable, often requiring dynamic programming or greedy algorithms as solutions.
### 5. &nbsp; Logarithmic Order $O(\log n)$ {data-toc-label="Logarithmic Order"}
### 5. &nbsp; Logarithmic Order $O(\log n)$ {data-toc-label="5. &nbsp; Logarithmic Order"}
In contrast to exponential order, logarithmic order reflects situations where "the size is halved each round." Given an input data size $n$, since the size is halved each round, the number of iterations is $\log_2 n$, the inverse function of $2^n$.
@ -2861,7 +2861,7 @@ Logarithmic order is typical in algorithms based on the divide-and-conquer strat
This means the base $m$ can be changed without affecting the complexity. Therefore, we often omit the base $m$ and simply denote logarithmic order as $O(\log n)$.
### 6. &nbsp; Linear-Logarithmic Order $O(n \log n)$ {data-toc-label="Linear-Logarithmic Order"}
### 6. &nbsp; Linear-Logarithmic Order $O(n \log n)$ {data-toc-label="6. &nbsp; Linear-Logarithmic Order"}
Linear-logarithmic order often appears in nested loops, with the complexities of the two loops being $O(\log n)$ and $O(n)$ respectively. The related code is as follows:
@ -3076,7 +3076,7 @@ The image below demonstrates how linear-logarithmic order is generated. Each lev
Mainstream sorting algorithms typically have a time complexity of $O(n \log n)$, such as quicksort, mergesort, and heapsort.
### 7. &nbsp; Factorial Order $O(n!)$ {data-toc-label="Factorial Order"}
### 7. &nbsp; Factorial Order $O(n!)$ {data-toc-label="7. &nbsp; Factorial Order"}
Factorial order corresponds to the mathematical problem of "full permutation." Given $n$ distinct elements, the total number of possible permutations is:

4
en/docs/chapter_data_structure/index.md
View file

@ -5,12 +5,8 @@ icon: material/shape-outline
# Chapter 3. &nbsp; Data Structures
<div class="center-table" markdown>
![Data Structures](../assets/covers/chapter_data_structure.jpg){ class="cover-image" }
</div>
!!! abstract
Data structures serve as a robust and diverse framework.

4
en/docs/chapter_hashing/index.md
View file

@ -5,12 +5,8 @@ icon: material/table-search
# Chapter 6. &nbsp; Hash Table
<div class="center-table" markdown>
![Hash Table](../assets/covers/chapter_hashing.jpg){ class="cover-image" }
</div>
!!! abstract
In the world of computing, a hash table is akin to an intelligent librarian.

4
en/docs/chapter_introduction/index.md
View file

@ -5,12 +5,8 @@ icon: material/calculator-variant-outline
# Chapter 1. &nbsp; Introduction to Algorithms
<div class="center-table" markdown>
![A first look at the algorithm](../assets/covers/chapter_introduction.jpg){ class="cover-image" }
</div>
!!! abstract
A graceful maiden dances, intertwined with the data, her skirt swaying to the melody of algorithms.

4
en/docs/chapter_preface/index.md
View file

@ -5,12 +5,8 @@ icon: material/book-open-outline
# Chapter 0. &nbsp; Preface
<div class="center-table" markdown>
![Preface](../assets/covers/chapter_preface.jpg){ class="cover-image" }
</div>
!!! abstract
Algorithms are like a beautiful symphony, with each line of code flowing like a rhythm.

4
en/docs/chapter_stack_and_queue/index.md
View file

@ -5,12 +5,8 @@ icon: material/stack-overflow
# Chapter 5. &nbsp; Stack and Queue
<div class="center-table" markdown>
![Stack and Queue](../assets/covers/chapter_stack_and_queue.jpg){ class="cover-image" }
</div>
!!! abstract
A stack is like cats placed on top of each other, while a queue is like cats lined up one by one.