Data Structures and Algorithms in C++ 开源项目教程

Data Structures and Algorithms in C++ 开源项目教程

Data-Structures-and-Algorithms-in-cppThis repository is in development phase and will soon provide you with c++ code of various data structures and algorithms项目地址:https://gitcode.com/gh_mirrors/da/Data-Structures-and-Algorithms-in-cpp

1. 项目的目录结构及介绍

目录结构

Data-Structures-and-Algorithms-in-cpp/
├── Arrays
│   ├── ArrayRotation.cpp
│   ├── KadaneAlgorithm.cpp
│   └── README.md
├── LinkedLists
│   ├── CircularLinkedList.cpp
│   ├── DoublyLinkedList.cpp
│   └── README.md
├── README.md
└── ...

目录介绍

  • Arrays: 包含与数组相关的算法实现,如数组旋转、Kadane算法等。
  • LinkedLists: 包含与链表相关的算法实现,如循环链表、双向链表等。
  • README.md: 项目的主说明文件,包含项目的基本信息和使用指南。

2. 项目的启动文件介绍

启动文件

项目的启动文件通常是指示用户如何开始使用项目的文件。在本项目中,主要的启动文件是各个子目录下的 README.md 文件。

示例

Arrays 目录为例:

# Arrays

本目录包含与数组相关的算法实现。

## 文件列表

- **ArrayRotation.cpp**: 实现数组旋转的算法。
- **KadaneAlgorithm.cpp**: 实现Kadane算法的代码。

## 使用方法

1. 克隆项目到本地。
2. 进入 `Arrays` 目录。
3. 编译并运行所需的 `.cpp` 文件。

3. 项目的配置文件介绍

配置文件

本项目主要关注算法实现,因此没有特定的配置文件。所有必要的配置和参数通常直接在代码中定义。

示例

ArrayRotation.cpp 中,可能会看到如下配置:

const int n = 5; // 数组大小
int arr[n] = {1, 2, 3, 4, 5}; // 初始数组
int d = 2; // 旋转的步数

这些配置项直接在代码中定义,用户可以根据需要修改这些值来测试不同的输入。


以上是关于 Data Structures and Algorithms in C++ 开源项目的教程,涵盖了项目的目录结构、启动文件和配置文件的介绍。希望这些信息能帮助你更好地理解和使用该项目。

Data-Structures-and-Algorithms-in-cppThis repository is in development phase and will soon provide you with c++ code of various data structures and algorithms项目地址:https://gitcode.com/gh_mirrors/da/Data-Structures-and-Algorithms-in-cpp

创作声明:本文部分内容由AI辅助生成(AIGC),仅供参考

The fourth edition of Data Structures and Algorithm Analysis in C++ describes data structures, methods of organizing large amounts of data, and algorithm analysis, the estimation of the running time of algorithms. As computers become faster and faster, the need for programs that can handle large amounts of input becomes more acute. Paradoxically, this requires more careful attention to efficiency, since inefficiencies in programs become most obvious when input sizes are large. By analyzing an algorithm before it is actually coded, students can decide if a particular solution will be feasible. For example, in this text students look at specific problems and see how careful implementations can reduce the time constraint for large amounts of data from centuries to less than a second. Therefore, no algorithm or data structure is presented without an explanation of its running time. In some cases, minute details that affect the running time of the implementation are explored. Once a solution method is determined, a program must still be written. As computers have become more powerful, the problems they must solve have become larger and more complex, requiring development of more intricate programs. The goal of this text is to teach students good programming and algorithm analysis skills simultaneously so that they can develop such programs with the maximum amount of efficiency. This book is suitable for either an advanced data structures course or a first-year graduate course in algorithm analysis. Students should have some knowledge of intermediate programming, including such topics as pointers, recursion, and object-based programming, as well as some background in discrete math.
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