mirror of
https://github.com/krahets/hello-algo.git
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feat: Revised the book (#978)
* Sync recent changes to the revised Word. * Revised the preface chapter * Revised the introduction chapter * Revised the computation complexity chapter * Revised the chapter data structure * Revised the chapter array and linked list * Revised the chapter stack and queue * Revised the chapter hashing * Revised the chapter tree * Revised the chapter heap * Revised the chapter graph * Revised the chapter searching * Reivised the sorting chapter * Revised the divide and conquer chapter * Revised the chapter backtacking * Revised the DP chapter * Revised the greedy chapter * Revised the appendix chapter * Revised the preface chapter doubly * Revised the figures
This commit is contained in:
Yudong Jin
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# 一起参与创作
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由于作者能力有限，书中难免存在一些遗漏和错误，请您谅解。如果您发现了笔误、失效链接、内容缺失、文字歧义、解释不清晰或行文结构不合理等问题，请协助我们进行修正，以给读者提供更优质的学习资源。
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由于笔者能力有限，书中难免存在一些遗漏和错误，请您谅解。如果您发现了笔误、链接失效、内容缺失、文字歧义、解释不清晰或行文结构不合理等问题，请协助我们进行修正，以给读者提供更优质的学习资源。
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所有[撰稿人](https://github.com/krahets/hello-algo/graphs/contributors)的 GitHub ID 将被展示在本书的仓库主页上，以感谢他们对开源社区的无私奉献。
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所有[撰稿人](https://github.com/krahets/hello-algo/graphs/contributors)的 GitHub ID 将在本书仓库、网页版和 PDF 版的主页上进行展示，以感谢他们对开源社区的无私奉献。
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!!! success "开源的魅力"
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纸质书籍的两次印刷的间隔时间往往需要数年，内容更新非常不方便。
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纸质图书的两次印刷的间隔时间往往较久，内容更新非常不方便。
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然而在本开源书中，内容更迭的时间被缩短至数日甚至几个小时。
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而在本开源书中，内容更迭的时间被缩短至数日甚至几个小时。
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### 内容微调
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@@ -26,7 +26,7 @@
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如果您有兴趣参与此开源项目，包括将代码翻译成其他编程语言、扩展文章内容等，那么需要实施以下 Pull Request 工作流程。
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1. 登录 GitHub ，将[本仓库](https://github.com/krahets/hello-algo) Fork 到个人账号下。
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1. 登录 GitHub ，将本书的[代码仓库](https://github.com/krahets/hello-algo) Fork 到个人账号下。
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2. 进入您的 Fork 仓库网页，使用 `git clone` 命令将仓库克隆至本地。
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3. 在本地进行内容创作，并进行完整测试，验证代码的正确性。
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4. 将本地所做更改 Commit ，然后 Push 至远程仓库。
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@@ -34,13 +34,13 @@
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### Docker 部署
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在 `hello-algo` 根目录下，执行以下 Docker 脚本，即可在 `http://localhost:8000` 访问本项目。
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在 `hello-algo` 根目录下，执行以下 Docker 脚本，即可在 `http://localhost:8000` 访问本项目：
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```shell
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docker-compose up -d
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```
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使用以下命令即可删除部署。
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使用以下命令即可删除部署：
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```shell
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docker-compose down
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### VSCode
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本书推荐使用开源轻量的 VSCode 作为本地 IDE ，下载并安装 [VSCode](https://code.visualstudio.com/) 。
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本书推荐使用开源、轻量的 VSCode 作为本地 IDE ，下载并安装 [VSCode](https://code.visualstudio.com/) 。
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### Java 环境
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### C/C++ 环境
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1. Windows 系统需要安装 [MinGW](https://sourceforge.net/projects/mingw-w64/files/)（[配置教程](https://blog.csdn.net/qq_33698226/article/details/129031241)），MacOS 自带 Clang 无须安装。
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1. Windows 系统需要安装 [MinGW](https://sourceforge.net/projects/mingw-w64/files/)（[配置教程](https://blog.csdn.net/qq_33698226/article/details/129031241)）；MacOS 自带 Clang ，无须安装。
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2. 在 VSCode 的插件市场中搜索 `c++` ，安装 C/C++ Extension Pack 。
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3. （可选）打开 Settings 页面，搜索 `Clang_format_fallback Style` 代码格式化选项，设置为 `{ BasedOnStyle: Microsoft, BreakBeforeBraces: Attach }` 。
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1. 下载并安装 [go](https://go.dev/dl/) 。
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2. 在 VSCode 的插件市场中搜索 `go` ，安装 Go 。
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3. 快捷键 `Ctrl + Shift + P` 呼出命令栏，输入 go ，选择 `Go: Install/Update Tools` ，全部勾选并安装即可。
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3. 按快捷键 `Ctrl + Shift + P` 呼出命令栏，输入 go ，选择 `Go: Install/Update Tools` ，全部勾选并安装即可。
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### JavaScript 环境
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下表列出了书中出现的重要术语。建议你同时记住它们的中英文叫法，以便阅读英文文献。
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<p align="center"> 表 <id> 数据结构与算法重要名词 </p>
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<p align="center"> 表 <id> 数据结构与算法的重要名词 </p>
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| 中文 | English |
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| -------------- | ------------------------------ |
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| 算法 | algorithm |
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| 数据结构 | data structure |
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| 渐近复杂度分析 | asymptotic complexity analysis |
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| 时间复杂度 | time complexity |
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| 空间复杂度 | space complexity |
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| 迭代 | iteration |
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| 递归 | recursion |
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| 尾递归 | tail recursion |
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| 递归树 | recursion tree |
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| 大 $O$ 记号 | big-$O$ notation |
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| 渐近上界 | asymptotic upper bound |
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| 原码 | sign–magnitude |
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| 反码 | 1's complement |
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| 补码 | 2's complement |
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| 数组 | array |
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| 索引 | index |
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| 链表 | linked list |
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| 链表节点 | linked list node, list node |
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| 列表 | list |
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| 动态数组 | dynamic array |
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| 栈 | stack |
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| 队列 | queue |
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| 双向队列 | double-ended queue |
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| 哈希表 | hash table |
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| 桶 | bucket |
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| 哈希函数 | hash function |
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| 哈希冲突 | hash collision |
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| 负载因子 | load factor |
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| 链式地址 | separate chaining |
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| 开放寻址 | open addressing |
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| 线性探测 | linear probing |
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| 懒删除 | lazy deletion |
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| 二叉树 | binary tree |
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| 树节点 | tree node |
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| 左子节点 | left-child node |
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| 右子节点 | right-child node |
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| 父节点 | parent node |
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| 左子树 | left subtree |
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| 右子树 | right subtree |
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| 根节点 | root node |
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| 叶节点 | leaf node |
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| 边 | edge |
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| 层 | level |
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| 度 | degree |
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| 高度 | height |
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| 深度 | depth |
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| 完美二叉树 | perfect binary tree |
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| 完全二叉树 | complete binary tree |
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| 完满二叉树 | full binary tree |
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| 平衡二叉树 | balanced binary tree |
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| AVL 树 | AVL tree |
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| 红黑树 | red-black tree |
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| 层序遍历 | level-order traversal |
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| 广度优先遍历 | breadth-first traversal |
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| 深度优先遍历 | depth-first traversal |
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| 二叉搜索树 | binary search tree |
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| 平衡二叉搜索树 | balanced binary search tree |
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| 平衡因子 | balance factor |
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| 堆 | heap |
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| 大顶堆 | max heap |
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| 小顶堆 | min heap |
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| 优先队列 | priority queue |
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| 堆化 | heapify |
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| 图 | graph |
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| 顶点 | vertex |
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| 无向图 | undirected graph |
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| 有向图 | directed graph |
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| 连通图 | connected graph |
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| 非连通图 | disconnected graph |
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| 有权图 | weighted graph |
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| 邻接 | adjacency |
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| 路径 | path |
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| 入度 | in-degree |
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| 出度 | out-degree |
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| 邻接矩阵 | adjacency matrix |
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| 邻接表 | adjacency list |
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| 广度优先搜索 | breadth-first search |
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| 深度优先搜索 | depth-first search |
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| 二分查找 | binary search |
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| 搜索算法 | searching algorithm |
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| 排序算法 | sorting algorithm |
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| 选择排序 | selection sort |
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| 冒泡排序 | bubble sort |
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| 插入排序 | insertion sort |
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| 快速排序 | quick sort |
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| 归并排序 | merge sort |
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| 堆排序 | heap sort |
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| 桶排序 | bucket sort |
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| 计数排序 | counting sort |
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| 基数排序 | radix sort |
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| 分治 | divide and conquer |
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| 汉诺塔问题 | hanota problem |
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| 回溯算法 | backtracking algorithm |
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| 约束 | constraint |
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| 解 | solution |
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| 状态 | state |
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| 剪枝 | pruning |
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| 全排列问题 | permutations problem |
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| 子集和问题 | subset-sum problem |
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| N 皇后问题 | N-queens problem |
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| 动态规划 | dynamic programming |
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| 初始状态 | initial state |
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| 状态转移方程 | state-trasition equation |
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| 背包问题 | knapsack problem |
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| 编辑距离问题 | edit distance problem |
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| 贪心算法 | greedy algorithm |
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| 中文 | English | 中文 | English |
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| -------------- | ------------------------------ | -------------- | --------------------------- |
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| 算法 | algorithm | 层序遍历 | level-order traversal |
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| 数据结构 | data structure | 广度优先遍历 | breadth-first traversal |
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| 渐近复杂度分析 | asymptotic complexity analysis | 深度优先遍历 | depth-first traversal |
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| 时间复杂度 | time complexity | 二叉搜索树 | binary search tree |
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| 空间复杂度 | space complexity | 平衡二叉搜索树 | balanced binary search tree |
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| 迭代 | iteration | 平衡因子 | balance factor |
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| 递归 | recursion | 堆 | heap |
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| 尾递归 | tail recursion | 大顶堆 | max heap |
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| 递归树 | recursion tree | 小顶堆 | min heap |
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| 大 | big- | 优先队列 | priority queue |
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| 记号 | notation | | |
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| 渐近上界 | asymptotic upper bound | 堆化 | heapify |
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| 原码 | sign–magnitude | 图 | graph |
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| 反码 | 1’s complement | 顶点 | vertex |
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| 补码 | 2’s complement | 无向图 | undirected graph |
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| 数组 | array | 有向图 | directed graph |
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| 索引 | index | 连通图 | connected graph |
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| 链表 | linked list | 非连通图 | disconnected graph |
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| 链表节点 | linked list node, list node | 有权图 | weighted graph |
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| 列表 | list | 邻接 | adjacency |
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| 动态数组 | dynamic array | 路径 | path |
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| 硬盘 | hard disk | 入度 | in-degree |
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| 内存 | random-access memory (RAM) | 出度 | out-degree |
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| 缓存 | cache memory | 邻接矩阵 | adjacency matrix |
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| 缓存未命中 | cache miss | 邻接表 | adjacency list |
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| 缓存命中率 | cache hit rate | 广度优先搜索 | breadth-first search |
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| 栈 | stack | 深度优先搜索 | depth-first search |
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| 队列 | queue | 二分查找 | binary search |
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| 双向队列 | double-ended queue | 搜索算法 | searching algorithm |
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| 哈希表 | hash table | 排序算法 | sorting algorithm |
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| 桶 | bucket | 选择排序 | selection sort |
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| 哈希函数 | hash function | 冒泡排序 | bubble sort |
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| 哈希冲突 | hash collision | 插入排序 | insertion sort |
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| 负载因子 | load factor | 快速排序 | quick sort |
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| 链式地址 | separate chaining | 归并排序 | merge sort |
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| 开放寻址 | open addressing | 堆排序 | heap sort |
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| 线性探测 | linear probing | 桶排序 | bucket sort |
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| 懒删除 | lazy deletion | 计数排序 | counting sort |
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| 二叉树 | binary tree | 基数排序 | radix sort |
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| 树节点 | tree node | 分治 | divide and conquer |
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| 左子节点 | left-child node | 汉诺塔问题 | hanota problem |
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| 右子节点 | right-child node | 回溯算法 | backtracking algorithm |
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| 父节点 | parent node | 约束 | constraint |
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| 左子树 | left subtree | 解 | solution |
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| 右子树 | right subtree | 状态 | state |
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| 根节点 | root node | 剪枝 | pruning |
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| 叶节点 | leaf node | 全排列问题 | permutations problem |
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| 边 | edge | 子集和问题 | subset-sum problem |
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| 层 | level | N 皇后问题 | N-queens problem |
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| 度 | degree | 动态规划 | dynamic programming |
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| 高度 | height | 初始状态 | initial state |
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| 深度 | depth | 状态转移方程 | state-trasition equation |
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| 完美二叉树 | perfect binary tree | 背包问题 | knapsack problem |
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| 完全二叉树 | complete binary tree | 编辑距离问题 | edit distance problem |
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| 完满二叉树 | full binary tree | 贪心算法 | greedy algorithm |
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| 平衡二叉树 | balanced binary tree | | |
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| AVL 树 | AVL tree | | |
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| 红黑树 | red-black tree | | |
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Reference in New Issue
Block a user