From b3f100aff1ecff926488a896974cb861e37895ca Mon Sep 17 00:00:00 2001 From: Yudong Jin Date: Mon, 1 Apr 2024 19:37:00 +0800 Subject: [PATCH] Bug fixes and improvements (#1205) * Add Ruby code blocks to documents * Remove Ruby code from en/docs * Remove "center-table" class in index.md * Add "data-toc-label" to handle the latex heading during the build process * Use normal JD link instead. * Bug fixes --- codes/rust/chapter_sorting/merge_sort.rs | 2 +- docs/chapter_appendix/index.md | 4 ---- docs/chapter_backtracking/index.md | 4 ---- .../chapter_computational_complexity/index.md | 4 ---- .../space_complexity.md | 10 +++++----- .../time_complexity.md | 14 +++++++------- docs/chapter_data_structure/index.md | 4 ---- docs/chapter_divide_and_conquer/index.md | 4 ---- docs/chapter_dynamic_programming/index.md | 4 ---- docs/chapter_graph/index.md | 4 ---- docs/chapter_greedy/index.md | 4 ---- docs/chapter_hashing/index.md | 4 ---- docs/chapter_heap/index.md | 4 ---- docs/chapter_introduction/index.md | 4 ---- .../index.assets/book_jd_link.jpg | Bin 101011 -> 101787 bytes docs/chapter_paperbook/index.md | 2 +- docs/chapter_preface/index.md | 4 ---- docs/chapter_searching/index.md | 4 ---- docs/chapter_sorting/index.md | 4 ---- docs/chapter_stack_and_queue/index.md | 4 ---- .../array_representation_of_tree.md | 2 +- docs/chapter_tree/index.md | 4 ---- .../chapter_computational_complexity/index.md | 4 ---- .../space_complexity.md | 10 +++++----- .../time_complexity.md | 14 +++++++------- en/docs/chapter_data_structure/index.md | 4 ---- en/docs/chapter_hashing/index.md | 4 ---- en/docs/chapter_introduction/index.md | 4 ---- en/docs/chapter_preface/index.md | 4 ---- en/docs/chapter_stack_and_queue/index.md | 4 ---- 30 files changed, 27 insertions(+), 115 deletions(-) diff --git a/codes/rust/chapter_sorting/merge_sort.rs b/codes/rust/chapter_sorting/merge_sort.rs index 698af073f..02894bd33 100644 --- a/codes/rust/chapter_sorting/merge_sort.rs +++ b/codes/rust/chapter_sorting/merge_sort.rs @@ -15,7 +15,7 @@ fn merge(nums: &mut [i32], left: usize, mid: usize, right: usize) { // 当左右子数组都还有元素时,进行比较并将较小的元素复制到临时数组中 while i <= mid && j <= right { if nums[i] <= nums[j] { - tmp[k] = nums[j]; + tmp[k] = nums[i]; i += 1; } else { tmp[k] = nums[j]; diff --git a/docs/chapter_appendix/index.md b/docs/chapter_appendix/index.md index 55f077ee3..44aa38a95 100644 --- a/docs/chapter_appendix/index.md +++ b/docs/chapter_appendix/index.md @@ -1,7 +1,3 @@ # 附录 -
- ![附录](../assets/covers/chapter_appendix.jpg) - -
diff --git a/docs/chapter_backtracking/index.md b/docs/chapter_backtracking/index.md index cfaaba97c..68b6a3ae4 100644 --- a/docs/chapter_backtracking/index.md +++ b/docs/chapter_backtracking/index.md @@ -1,11 +1,7 @@ # 回溯 -
- ![回溯](../assets/covers/chapter_backtracking.jpg) -
- !!! 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。 diff --git a/docs/chapter_computational_complexity/index.md b/docs/chapter_computational_complexity/index.md index 7a3294d68..dcb3b499e 100644 --- a/docs/chapter_computational_complexity/index.md +++ b/docs/chapter_computational_complexity/index.md @@ -1,11 +1,7 @@ # 复杂度分析 -
- ![复杂度分析](../assets/covers/chapter_complexity_analysis.jpg) -
- !!! abstract 复杂度分析犹如浩瀚的算法宇宙中的时空向导。 diff --git a/docs/chapter_computational_complexity/space_complexity.md b/docs/chapter_computational_complexity/space_complexity.md index fb57deddb..fa8b1e66e 100755 --- a/docs/chapter_computational_complexity/space_complexity.md +++ b/docs/chapter_computational_complexity/space_complexity.md @@ -790,7 +790,7 @@ $$ ![常见的空间复杂度类型](space_complexity.assets/space_complexity_common_types.png) -### 常数阶 $O(1)$ {data-toc-label="常数阶"} +### 常数阶 $O(1)$ 常数阶常见于数量与输入数据大小 $n$ 无关的常量、变量、对象。 @@ -800,7 +800,7 @@ $$ [file]{space_complexity}-[class]{}-[func]{constant} ``` -### 线性阶 $O(n)$ {data-toc-label="线性阶"} +### 线性阶 $O(n)$ 线性阶常见于元素数量与 $n$ 成正比的数组、链表、栈、队列等: @@ -816,7 +816,7 @@ $$ ![递归函数产生的线性阶空间复杂度](space_complexity.assets/space_complexity_recursive_linear.png) -### 平方阶 $O(n^2)$ {data-toc-label="平方阶"} +### 平方阶 $O(n^2)$ 平方阶常见于矩阵和图,元素数量与 $n$ 成平方关系: @@ -832,7 +832,7 @@ $$ ![递归函数产生的平方阶空间复杂度](space_complexity.assets/space_complexity_recursive_quadratic.png) -### 指数阶 $O(2^n)$ {data-toc-label="指数阶"} +### 指数阶 $O(2^n)$ 指数阶常见于二叉树。观察下图,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: @@ -842,7 +842,7 @@ $$ ![满二叉树产生的指数阶空间复杂度](space_complexity.assets/space_complexity_exponential.png) -### 对数阶 $O(\log n)$ {data-toc-label="对数阶"} +### 对数阶 $O(\log n)$ 对数阶常见于分治算法。例如归并排序,输入长度为 $n$ 的数组,每轮递归将数组从中点处划分为两半,形成高度为 $\log n$ 的递归树,使用 $O(\log n)$ 栈帧空间。 diff --git a/docs/chapter_computational_complexity/time_complexity.md b/docs/chapter_computational_complexity/time_complexity.md index 9589795ce..e902eb7d8 100755 --- a/docs/chapter_computational_complexity/time_complexity.md +++ b/docs/chapter_computational_complexity/time_complexity.md @@ -1033,7 +1033,7 @@ $$ ![常见的时间复杂度类型](time_complexity.assets/time_complexity_common_types.png) -### 常数阶 $O(1)$ {data-toc-label="常数阶"} +### 常数阶 $O(1)$ 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。 @@ -1043,7 +1043,7 @@ $$ [file]{time_complexity}-[class]{}-[func]{constant} ``` -### 线性阶 $O(n)$ {data-toc-label="线性阶"} +### 线性阶 $O(n)$ 线性阶的操作数量相对于输入数据大小 $n$ 以线性级别增长。线性阶通常出现在单层循环中: @@ -1059,7 +1059,7 @@ $$ 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。 -### 平方阶 $O(n^2)$ {data-toc-label="平方阶"} +### 平方阶 $O(n^2)$ 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 $O(n^2)$ : @@ -1077,7 +1077,7 @@ $$ [file]{time_complexity}-[class]{}-[func]{bubble_sort} ``` -### 指数阶 $O(2^n)$ {data-toc-label="指数阶"} +### 指数阶 $O(2^n)$ 生物学的“细胞分裂”是指数阶增长的典型例子:初始状态为 $1$ 个细胞,分裂一轮后变为 $2$ 个,分裂两轮后变为 $4$ 个,以此类推,分裂 $n$ 轮后有 $2^n$ 个细胞。 @@ -1097,7 +1097,7 @@ $$ 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。 -### 对数阶 $O(\log n)$ {data-toc-label="对数阶"} +### 对数阶 $O(\log n)$ 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。 @@ -1127,7 +1127,7 @@ $$ 也就是说,底数 $m$ 可以在不影响复杂度的前提下转换。因此我们通常会省略底数 $m$ ,将对数阶直接记为 $O(\log n)$ 。 -### 线性对数阶 $O(n \log n)$ {data-toc-label="线性对数阶"} +### 线性对数阶 $O(n \log n)$ 线性对数阶常出现于嵌套循环中,两层循环的时间复杂度分别为 $O(\log n)$ 和 $O(n)$ 。相关代码如下: @@ -1141,7 +1141,7 @@ $$ 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。 -### 阶乘阶 $O(n!)$ {data-toc-label="阶乘阶"} +### 阶乘阶 $O(n!)$ 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: diff --git a/docs/chapter_data_structure/index.md b/docs/chapter_data_structure/index.md index d4ade8f8f..fb1dced52 100644 --- a/docs/chapter_data_structure/index.md +++ b/docs/chapter_data_structure/index.md @@ -1,11 +1,7 @@ # 数据结构 -
- ![数据结构](../assets/covers/chapter_data_structure.jpg) -
- !!! abstract 数据结构如同一副稳固而多样的框架。 diff --git a/docs/chapter_divide_and_conquer/index.md b/docs/chapter_divide_and_conquer/index.md index 1d828f090..5528a7592 100644 --- a/docs/chapter_divide_and_conquer/index.md +++ b/docs/chapter_divide_and_conquer/index.md @@ -1,11 +1,7 @@ # 分治 -
- ![分治](../assets/covers/chapter_divide_and_conquer.jpg) -
- !!! abstract 难题被逐层拆解,每一次的拆解都使它变得更为简单。 diff --git a/docs/chapter_dynamic_programming/index.md b/docs/chapter_dynamic_programming/index.md index b27ede90c..a5e0e585f 100644 --- a/docs/chapter_dynamic_programming/index.md +++ b/docs/chapter_dynamic_programming/index.md @@ -1,11 +1,7 @@ # 动态规划 -
- ![动态规划](../assets/covers/chapter_dynamic_programming.jpg) -
- !!! abstract 小溪汇入河流,江河汇入大海。 diff --git a/docs/chapter_graph/index.md b/docs/chapter_graph/index.md index 36f4036a6..10318079b 100644 --- a/docs/chapter_graph/index.md +++ b/docs/chapter_graph/index.md @@ -1,11 +1,7 @@ # 图 -
- ![图](../assets/covers/chapter_graph.jpg) -
- !!! 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。 diff --git a/docs/chapter_greedy/index.md b/docs/chapter_greedy/index.md index be2bc75be..69d9075b3 100644 --- a/docs/chapter_greedy/index.md +++ b/docs/chapter_greedy/index.md @@ -1,11 +1,7 @@ # 贪心 -
- ![贪心](../assets/covers/chapter_greedy.jpg) -
- !!! abstract 向日葵朝着太阳转动,时刻追求自身成长的最大可能。 diff --git a/docs/chapter_hashing/index.md b/docs/chapter_hashing/index.md index 2adbcd7f3..6d5b2528a 100644 --- a/docs/chapter_hashing/index.md +++ b/docs/chapter_hashing/index.md @@ -1,11 +1,7 @@ # 哈希表 -
- ![哈希表](../assets/covers/chapter_hashing.jpg) -
- !!! abstract 在计算机世界中,哈希表如同一位聪慧的图书管理员。 diff --git a/docs/chapter_heap/index.md b/docs/chapter_heap/index.md index ddb7c0ba9..3aeb408e1 100644 --- a/docs/chapter_heap/index.md +++ b/docs/chapter_heap/index.md @@ -1,11 +1,7 @@ # 堆 -
- ![堆](../assets/covers/chapter_heap.jpg) -
- !!! abstract 堆就像是山岳峰峦,层叠起伏、形态各异。 diff --git a/docs/chapter_introduction/index.md b/docs/chapter_introduction/index.md index adc443460..5d7839e29 100644 --- a/docs/chapter_introduction/index.md +++ b/docs/chapter_introduction/index.md @@ -1,11 +1,7 @@ # 初识算法 -
- ![初识算法](../assets/covers/chapter_introduction.jpg) -
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z=Zu>ZoQhd$mD&~6*0;1LQk3Iz9zvyqk_ZirxGI?gbUesQ_rU0L(r6Yu4C^!P3K z`>*EOT^SNoXyH;SGTapNmWyk%h5B8A7dX~$gLiF|UczIZy*f6) zJn~XFbLe8Bbv*E7dfyz-*qN zsArhLm{iF>-W{A-mFd^hS-G9>~JP-g0EyL@9YzJV3Z(SZc7;_sKW*s%A(<`n3~f@ggA#kUgs)+1^UJ zyJLW!Lx=(&e)E#rWI|R-?v!S)o)ssvbrutTTegpvnIrBxi^zX&D-2&+cWiQ~jXsK% zH&x&xO6L$rU;EM4HU1fsOqL=7cK+uJ)U#q{MY zRig6!3A^6@)g=wh=?v>P!2Dg~p2WHZ@l-o2sW2B@0J8Sm^R7TkNc~j`vYmFuPfkkw z;l)F59AFpgo1S(xDpfM@6LyqKITyOR{T7XCw6&eAIM;l&uF5Zd$TKz3it)1=oHXCA z@j81&;Bsr`sWWm)Tx4+@0Gi{hO#9Z*!8=-R|J8l`qzw14ooC!+?2gya9>0dX8fb4B SPv#{Z$ - ![前言](../assets/covers/chapter_preface.jpg) - - !!! abstract 算法犹如美妙的交响乐,每一行代码都像韵律般流淌。 diff --git a/docs/chapter_searching/index.md b/docs/chapter_searching/index.md index 8619d00c5..f473aaf8f 100644 --- a/docs/chapter_searching/index.md +++ b/docs/chapter_searching/index.md @@ -1,11 +1,7 @@ # 搜索 -
- ![搜索](../assets/covers/chapter_searching.jpg) -
- !!! 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。 diff --git a/docs/chapter_sorting/index.md b/docs/chapter_sorting/index.md index 11d083256..41c7dece9 100644 --- a/docs/chapter_sorting/index.md +++ b/docs/chapter_sorting/index.md @@ -1,11 +1,7 @@ # 排序 -
- ![排序](../assets/covers/chapter_sorting.jpg) -
- !!! 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。 diff --git a/docs/chapter_stack_and_queue/index.md b/docs/chapter_stack_and_queue/index.md index 08d4f8d38..ec053f82c 100644 --- a/docs/chapter_stack_and_queue/index.md +++ b/docs/chapter_stack_and_queue/index.md @@ -1,11 +1,7 @@ # 栈与队列 -
- ![栈与队列](../assets/covers/chapter_stack_and_queue.jpg) -
- !!! abstract 栈如同叠猫猫,而队列就像猫猫排队。 diff --git a/docs/chapter_tree/array_representation_of_tree.md b/docs/chapter_tree/array_representation_of_tree.md index ad7619345..87e077f19 100644 --- a/docs/chapter_tree/array_representation_of_tree.md +++ b/docs/chapter_tree/array_representation_of_tree.md @@ -12,7 +12,7 @@ ![完美二叉树的数组表示](array_representation_of_tree.assets/array_representation_binary_tree.png) -**映射公式的角色相当于链表中的引用**。给定数组中的任意一个节点,我们都可以通过映射公式来访问它的左(右)子节点。 +**映射公式的角色相当于链表中的节点引用(指针)**。给定数组中的任意一个节点,我们都可以通过映射公式来访问它的左(右)子节点。 ## 表示任意二叉树 diff --git a/docs/chapter_tree/index.md b/docs/chapter_tree/index.md index 2d3f2204d..c12f050f1 100644 --- a/docs/chapter_tree/index.md +++ b/docs/chapter_tree/index.md @@ -1,11 +1,7 @@ # 树 -
- ![树](../assets/covers/chapter_tree.jpg) -
- !!! abstract 参天大树充满生命力,根深叶茂,分枝扶疏。 diff --git a/en/docs/chapter_computational_complexity/index.md b/en/docs/chapter_computational_complexity/index.md index 3bc923aff..6ddc3421c 100644 --- a/en/docs/chapter_computational_complexity/index.md +++ b/en/docs/chapter_computational_complexity/index.md @@ -1,11 +1,7 @@ # Complexity Analysis -
- ![complexity_analysis](../assets/covers/chapter_complexity_analysis.jpg) -
- !!! abstract Complexity analysis is like a space-time navigator in the vast universe of algorithms. diff --git a/en/docs/chapter_computational_complexity/space_complexity.md b/en/docs/chapter_computational_complexity/space_complexity.md index 85f5f08d0..229eaa31f 100644 --- a/en/docs/chapter_computational_complexity/space_complexity.md +++ b/en/docs/chapter_computational_complexity/space_complexity.md @@ -736,7 +736,7 @@ $$ ![Common Types of Space Complexity](space_complexity.assets/space_complexity_common_types.png) -### Constant Order $O(1)$ {data-toc-label="Constant Order"} +### Constant Order $O(1)$ Constant order is common in constants, variables, objects that are independent of the size of input data $n$. @@ -746,7 +746,7 @@ Note that memory occupied by initializing variables or calling functions in a lo [file]{space_complexity}-[class]{}-[func]{constant} ``` -### Linear Order $O(n)$ {data-toc-label="Linear Order"} +### Linear Order $O(n)$ Linear order is common in arrays, linked lists, stacks, queues, etc., where the number of elements is proportional to $n$: @@ -762,7 +762,7 @@ As shown below, this function's recursive depth is $n$, meaning there are $n$ in ![Recursive Function Generating Linear Order Space Complexity](space_complexity.assets/space_complexity_recursive_linear.png) -### Quadratic Order $O(n^2)$ {data-toc-label="Quadratic Order"} +### Quadratic Order $O(n^2)$ Quadratic order is common in matrices and graphs, where the number of elements is quadratic to $n$: @@ -778,7 +778,7 @@ As shown below, the recursive depth of this function is $n$, and in each recursi ![Recursive Function Generating Quadratic Order Space Complexity](space_complexity.assets/space_complexity_recursive_quadratic.png) -### Exponential Order $O(2^n)$ {data-toc-label="Exponential Order"} +### Exponential Order $O(2^n)$ 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: @@ -788,7 +788,7 @@ Exponential order is common in binary trees. Observe the below image, a "full bi ![Full Binary Tree Generating Exponential Order Space Complexity](space_complexity.assets/space_complexity_exponential.png) -### Logarithmic Order $O(\log n)$ {data-toc-label="Logarithmic Order"} +### Logarithmic Order $O(\log n)$ 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. diff --git a/en/docs/chapter_computational_complexity/time_complexity.md b/en/docs/chapter_computational_complexity/time_complexity.md index 549d1d7bf..cb75e90e0 100644 --- a/en/docs/chapter_computational_complexity/time_complexity.md +++ b/en/docs/chapter_computational_complexity/time_complexity.md @@ -962,7 +962,7 @@ $$ ![Common Types of Time Complexity](time_complexity.assets/time_complexity_common_types.png) -### Constant Order $O(1)$ {data-toc-label="Constant Order"} +### Constant Order $O(1)$ 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 $n$: @@ -970,7 +970,7 @@ Constant order means the number of operations is independent of the input data s [file]{time_complexity}-[class]{}-[func]{constant} ``` -### Linear Order $O(n)$ {data-toc-label="Linear Order"} +### Linear Order $O(n)$ Linear order indicates the number of operations grows linearly with the input data size $n$. Linear order commonly appears in single-loop structures: @@ -986,7 +986,7 @@ 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. -### Quadratic Order $O(n^2)$ {data-toc-label="Quadratic Order"} +### Quadratic Order $O(n^2)$ 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 $O(n^2)$: @@ -1004,7 +1004,7 @@ For instance, in bubble sort, the outer loop runs $n - 1$ times, and the inner l [file]{time_complexity}-[class]{}-[func]{bubble_sort} ``` -### Exponential Order $O(2^n)$ {data-toc-label="Exponential Order"} +### Exponential Order $O(2^n)$ 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. @@ -1024,7 +1024,7 @@ 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. -### Logarithmic Order $O(\log n)$ {data-toc-label="Logarithmic Order"} +### Logarithmic Order $O(\log n)$ 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 $2^n$. @@ -1054,7 +1054,7 @@ 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 $O(\log n)$. -### Linear-Logarithmic Order $O(n \log n)$ {data-toc-label="Linear-Logarithmic Order"} +### Linear-Logarithmic Order $O(n \log n)$ 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: @@ -1068,7 +1068,7 @@ 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. -### Factorial Order $O(n!)$ {data-toc-label="Factorial Order"} +### Factorial Order $O(n!)$ Factorial order corresponds to the mathematical problem of "full permutation." Given $n$ distinct elements, the total number of possible permutations is: diff --git a/en/docs/chapter_data_structure/index.md b/en/docs/chapter_data_structure/index.md index a5d3d8c17..ef69156be 100644 --- a/en/docs/chapter_data_structure/index.md +++ b/en/docs/chapter_data_structure/index.md @@ -1,11 +1,7 @@ # Data Structures -
- ![Data Structures](../assets/covers/chapter_data_structure.jpg) -
- !!! abstract Data structures serve as a robust and diverse framework. diff --git a/en/docs/chapter_hashing/index.md b/en/docs/chapter_hashing/index.md index 260166b16..7f32ef50b 100644 --- a/en/docs/chapter_hashing/index.md +++ b/en/docs/chapter_hashing/index.md @@ -1,11 +1,7 @@ # Hash Table -
- ![Hash Table](../assets/covers/chapter_hashing.jpg) -
- !!! abstract In the world of computing, a hash table is akin to an intelligent librarian. diff --git a/en/docs/chapter_introduction/index.md b/en/docs/chapter_introduction/index.md index e85a23b36..54ec7d0be 100644 --- a/en/docs/chapter_introduction/index.md +++ b/en/docs/chapter_introduction/index.md @@ -1,11 +1,7 @@ # Introduction to Algorithms -
- ![A first look at the algorithm](../assets/covers/chapter_introduction.jpg) -
- !!! abstract A graceful maiden dances, intertwined with the data, her skirt swaying to the melody of algorithms. diff --git a/en/docs/chapter_preface/index.md b/en/docs/chapter_preface/index.md index 94d803cf7..332d997c7 100644 --- a/en/docs/chapter_preface/index.md +++ b/en/docs/chapter_preface/index.md @@ -1,11 +1,7 @@ # Preface -
- ![Preface](../assets/covers/chapter_preface.jpg) -
- !!! abstract Algorithms are like a beautiful symphony, with each line of code flowing like a rhythm. diff --git a/en/docs/chapter_stack_and_queue/index.md b/en/docs/chapter_stack_and_queue/index.md index d1d0e16ea..0a730ee78 100644 --- a/en/docs/chapter_stack_and_queue/index.md +++ b/en/docs/chapter_stack_and_queue/index.md @@ -1,11 +1,7 @@ # Stack and Queue -
- ![Stack and Queue](../assets/covers/chapter_stack_and_queue.jpg) -
- !!! abstract A stack is like cats placed on top of each other, while a queue is like cats lined up one by one.