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16.2   一起参与创作

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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第 16 章   附录

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16.1   编程环境安装

16.1.1   安装 IDE

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16.3   术语表

表 16-1 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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4.1   数组

「数组 array」是一种线性数据结构,其将相同类型的元素存储在连续的内存空间中。我们将元素在数组中的位置称为该元素的「索引 index」。图 4-1 展示了数组的主要术语和概念。

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第 4 章   数组与链表

数组与链表

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4.2   链表

内存空间是所有程序的公共资源,在一个复杂的系统运行环境下,空闲的内存空间可能散落在内存各处。我们知道,存储数组的内存空间必须是连续的,而当数组非常大时,内存可能无法提供如此大的连续空间。此时链表的灵活性优势就体现出来了。

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4.3   列表

「列表 list」是一个抽象的数据结构概念,它表示元素的有序集合,支持元素访问、修改、添加、删除和遍历等操作,无须使用者考虑容量限制的问题。列表可以基于链表或数组实现。

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4.4   内存与缓存 *

在本章的前两节中,我们探讨了数组和链表这两种基础且重要的数据结构,它们分别代表了“连续存储”和“分散存储”这两种不同的物理结构。

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4.5   小结

1.   重点回顾

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13.1   回溯算法

「回溯算法 backtracking algorithm」是一种通过穷举来解决问题的方法,它的核心思想是从一个初始状态出发,暴力搜索所有可能的解决方案,当遇到正确的解则将其记录,直到找到解或者尝试了所有可能的选择都无法找到解为止。

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第 13 章   回溯

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13.4   N 皇后问题

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13.2   全排列问题

全排列问题是回溯算法的一个典型应用。它的定义是在给定一个集合(如一个数组或字符串)的情况下,找出其中元素的所有可能的排列。

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13.3   子集和问题

13.3.1   无重复元素的情况

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13.5   小结

1.   重点回顾

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第 2 章   复杂度分析

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2.2   迭代与递归

在算法中,重复执行某个任务是很常见的,其与复杂度分析息息相关。因此,在展开介绍时间复杂度和空间复杂度之前,我们先来了解如何在程序中实现重复执行任务,即两种基本的程序控制结构:迭代、递归。

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2.1   算法效率评估

在算法设计中,我们先后追求以下两个层面的目标。

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2.4   空间复杂度

「空间复杂度 space complexity」用于衡量算法占用内存空间随着数据量变大时的增长趋势。这个概念与时间复杂度非常类似,只需将“运行时间”替换为“占用内存空间”。

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2.5   小结

1.   重点回顾

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2.3   时间复杂度

运行时间可以直观且准确地反映算法的效率。如果我们想准确预估一段代码的运行时间,应该如何操作呢?

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3.2   基本数据类型

当谈及计算机中的数据时,我们会想到文本、图片、视频、语音、3D 模型等各种形式。尽管这些数据的组织形式各异,但它们都由各种基本数据类型构成。

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3.4   字符编码 *

在计算机中,所有数据都是以二进制数的形式存储的,字符 char 也不例外。为了表示字符,我们需要建立一套“字符集”,规定每个字符和二进制数之间的一一对应关系。有了字符集之后,计算机就可以通过查表完成二进制数到字符的转换。

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3.1   数据结构分类

常见的数据结构包括数组、链表、栈、队列、哈希表、树、堆、图,它们可以从“逻辑结构”和“物理结构”两个维度进行分类。

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第 3 章   数据结构

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3.3   数字编码 *

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3.5   小结

1.   重点回顾

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12.2   分治搜索策略

我们已经学过,搜索算法分为两大类。

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12.3   构建二叉树问题

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12.1   分治算法

「分治 divide and conquer」,全称分而治之,是一种非常重要且常见的算法策略。分治通常基于递归实现,包括“分”和“治”两个步骤。

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12.4   汉诺塔问题

在归并排序和构建二叉树中,我们都是将原问题分解为两个规模为原问题一半的子问题。然而对于汉诺塔问题,我们采用不同的分解策略。

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第 12 章   分治

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12.5   小结