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<title>Before starting - Hello Algo</title>
<title>Before Starting - Hello Algo</title>
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<div class="md-header__topic" data-md-component="header-topic">
<span class="md-ellipsis">
Before starting
Before Starting
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<span class="md-ellipsis">
Before starting
Before Starting
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<span class="md-nav__icon md-icon"></span>
Before starting
Before Starting
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<span class="md-ellipsis">
0.1 About this book
0.1 About This Book
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<span class="md-ellipsis">
0.2 How to read
0.2 How to Use This Book
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<span class="md-ellipsis">
Chapter 1. Encounter with algorithms
Chapter 1. Encounter With Algorithms
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Chapter 1. Encounter with algorithms
Chapter 1. Encounter With Algorithms
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<span class="md-ellipsis">
1.1 Algorithms are everywhere
1.1 Algorithms Are Everywhere
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<span class="md-ellipsis">
1.2 What is an algorithm
1.2 What Is an Algorithm
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Chapter 2. Complexity analysis
Chapter 2. Complexity Analysis
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Chapter 2. Complexity analysis
Chapter 2. Complexity Analysis
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<span class="md-ellipsis">
2.1 Algorithm efficiency assessment
2.1 Algorithm Efficiency Evaluation
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<span class="md-ellipsis">
2.2 Iteration and recursion
2.2 Iteration and Recursion
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<span class="md-ellipsis">
2.3 Time complexity
2.3 Time Complexity
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2.4 Space complexity
2.4 Space Complexity
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Chapter 3. Data structures
Chapter 3. Data Structures
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Chapter 3. Data structures
Chapter 3. Data Structures
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3.1 Classification of data structures
3.1 Classification of Data Structures
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<span class="md-ellipsis">
3.2 Basic data types
3.2 Basic Data Types
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3.3 Number encoding *
3.3 Number Encoding *
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3.4 Character encoding *
3.4 Character Encoding *
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Chapter 4. Array and linked list
Chapter 4. Array and Linked List
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Chapter 4. Array and linked list
Chapter 4. Array and Linked List
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4.2 Linked list
4.2 Linked List
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4.4 Memory and cache *
4.4 Memory and Cache *
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Chapter 5. Stack and queue
Chapter 5. Stack and Queue
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Chapter 5. Stack and queue
Chapter 5. Stack and Queue
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5.3 Double-ended queue
5.3 Double-Ended Queue
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Chapter 6. Hash table
Chapter 6. Hashing
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Chapter 6. Hash table
Chapter 6. Hashing
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6.1 Hash table
6.1 Hash Table
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6.2 Hash collision
6.2 Hash Collision
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6.3 Hash algorithm
6.3 Hash Algorithm
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7.1 Binary tree
7.1 Binary Tree
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<span class="md-ellipsis">
7.2 Binary tree traversal
7.2 Binary Tree Traversal
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7.3 Array Representation of tree
7.3 Array Representation of Tree
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<span class="md-ellipsis">
7.4 Binary Search tree
7.4 Binary Search Tree
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<span class="md-ellipsis">
7.5 AVL tree *
7.5 AVL Tree *
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8.2 Building a heap
8.2 Building a Heap
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8.3 Top-k problem
8.3 Top-K Problem
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9.2 Basic graph operations
9.2 Basic Operations on Graphs
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9.3 Graph traversal
9.3 Graph Traversal
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10.1 Binary search
10.1 Binary Search
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10.2 Binary search insertion
10.2 Binary Search Insertion
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10.3 Binary search boundaries
10.3 Binary Search Edge Cases
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10.4 Hashing optimization strategies
10.4 Hash Optimization Strategy
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10.5 Search algorithms revisited
10.5 Search Algorithms Revisited
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11.1 Sorting algorithms
11.1 Sorting Algorithms
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11.2 Selection sort
11.2 Selection Sort
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11.3 Bubble sort
11.3 Bubble Sort
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11.4 Insertion sort
11.4 Insertion Sort
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11.5 Quick sort
11.5 Quick Sort
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11.6 Merge sort
11.6 Merge Sort
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11.7 Heap sort
11.7 Heap Sort
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11.8 Bucket sort
11.8 Bucket Sort
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11.9 Counting sort
11.9 Counting Sort
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<span class="md-ellipsis">
11.10 Radix sort
11.10 Radix Sort
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<span class="md-ellipsis">
Chapter 12. Divide and conquer
Chapter 12. Divide and Conquer
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Chapter 12. Divide and conquer
Chapter 12. Divide and Conquer
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12.1 Divide and conquer algorithms
12.1 Divide and Conquer Algorithms
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12.2 Divide and conquer search strategy
12.2 Divide and Conquer Search Strategy
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12.3 Building binary tree problem
12.3 Building a Binary Tree Problem
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12.4 Tower of Hanoi Problem
12.4 Hanoi Tower Problem
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<span class="md-ellipsis">
13.1 Backtracking algorithms
13.1 Backtracking Algorithm
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<span class="md-ellipsis">
13.2 Permutation problem
13.2 Permutations Problem
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13.3 Subset sum problem
13.3 Subset-Sum Problem
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<span class="md-ellipsis">
13.4 n queens problem
13.4 N-Queens Problem
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<span class="md-ellipsis">
Chapter 14. Dynamic programming
Chapter 14. Dynamic Programming
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Chapter 14. Dynamic programming
Chapter 14. Dynamic Programming
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14.1 Introduction to dynamic programming
14.1 Introduction to Dynamic Programming
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14.2 Characteristics of DP problems
14.2 Characteristics of Dynamic Programming Problems
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14.3 DP problem-solving approach
14.3 Dynamic Programming Problem-Solving Approach
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14.4 0-1 Knapsack problem
14.4 0-1 Knapsack Problem
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<span class="md-ellipsis">
14.5 Unbounded knapsack problem
14.5 Unbounded Knapsack Problem
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<span class="md-ellipsis">
14.6 Edit distance problem
14.6 Edit Distance Problem
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<span class="md-ellipsis">
15.1 Greedy algorithms
15.1 Greedy Algorithm
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<span class="md-ellipsis">
15.2 Fractional knapsack problem
15.2 Fractional Knapsack Problem
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15.3 Maximum capacity problem
15.3 Maximum Capacity Problem
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<span class="md-ellipsis">
15.4 Maximum product cutting problem
15.4 Maximum Product Cutting Problem
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16.1 Installation
16.1 Programming Environment Installation
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16.2 Contributing
16.2 Contributing Together
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16.3 Terminology
16.3 Terminology Table
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<!-- Page content -->
<h1 id="before-starting">Before starting<a class="headerlink" href="#before-starting" title="Permanent link">&para;</a></h1>
<p>A few years ago, I shared the "Sword for Offer" problem solutions on LeetCode, receiving encouragement and support from many readers. During interactions with readers, the most common question I encountered was "how to get started with algorithms." Gradually, I developed a keen interest in this question.</p>
<p>Directly solving problems seems to be the most popular method — it's simple, direct, and effective. However, problem-solving is like playing Minesweeper: those with strong self-study skills can navigate the pitfalls one by one, while those lacking a solid foundation may find themselves repeatedly stumbling and retreating in frustration. Reading through textbooks is also a common practice, but for job seekers, writing graduation thesis, submitting resumes, preparing for written tests and interviews have already consumed most of their energy, and reading thick books often becomes a daunting challenge.</p>
<p>If you're facing similar troubles, then this book is lucky to have found you. This book is my answer to the question. While it may not be the best solution, it is at least a positive attempt. Although this book is not enough to get you an offer directly, it will guide you to explore the "knowledge map" of data structures and algorithms, help you understand the shapes, sizes, and locations of different "mines", and enable you to master various "mine clearance methods". With these skills, I believe you can solve problems and read literature more comfortably, gradually building a knowledge system.</p>
<p>I deeply agree with Professor Feynman's statement: "Knowledge isn't free. You have to pay attention." In this sense, this book is not entirely "free." In order to live up to your precious "attention" for this book, I will do my best and devote my greatest "attention" to write this book.</p>
<p>Aware of my limitations, I recognize that despite the content of this book being refined over time, errors surely remain. I sincerely welcome critiques and corrections from both teachers and students.</p>
<p><a class="glightbox" href="../assets/covers/chapter_hello_algo.jpg" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="Hello Algo" class="cover-image" src="../assets/covers/chapter_hello_algo.jpg" /></a></p>
<h1 id="before-starting">Before Starting<a class="headerlink" href="#before-starting" title="Permanent link">&para;</a></h1>
<p>A few years ago, I shared the "Sword for Offer" problem solutions on LeetCode, receiving encouragement and support from many readers. During interactions with readers, the most frequently asked question I encountered was "how to get started with algorithms." Gradually, I developed a keen interest in this question.</p>
<p>Diving straight into problem-solving seems to be the most popular approach—it's simple, direct, and effective. However, problem-solving is like playing Minesweeper: those with strong self-learning abilities can successfully defuse the mines one by one, while those with insufficient foundations may end up bruised and battered, retreating step by step in frustration. Reading through textbooks is also a common practice, but for job seekers, graduation theses, resume submissions, and preparations for written tests and interviews have already consumed most of their energy, making working through thick books an arduous challenge.</p>
<p>If you're facing similar struggles, then it's fortunate that this book has "found" you. This book is my answer to this question—even if it may not be the optimal solution, it is at least a positive attempt. While this book alone won't directly land you a job offer, it will guide you to explore the "knowledge map" of data structures and algorithms, help you understand the shapes, sizes, and distributions of different "mines," and enable you to master various "mine-clearing methods." With these skills, I believe you can tackle problems and read technical literature more confidently, gradually building a complete knowledge system.</p>
<p>I deeply agree with Professor Feynman's words: "Knowledge isn't free. You have to pay attention." In this sense, this book is not entirely "free." In order to live up to the precious "attention" you invest in this book, I will do my utmost and devote my greatest "attention" to completing this work.</p>
<p>I'm acutely aware of my limited knowledge and shallow expertise. Although the content of this book has been refined over a period of time, there are certainly still many errors, and I sincerely welcome critiques and corrections from teachers and fellow students.</p>
<p><a class="glightbox" href="../assets/covers/chapter_hello_algo.jpg" data-type="image" data-width="100%" data-height="auto" data-desc-position="bottom"><img alt="Hello Algorithms" class="cover-image" src="../assets/covers/chapter_hello_algo.jpg" /></a></p>
<div style="text-align: center;">
<h2 style="margin-top: 0.8em; margin-bottom: 0.8em;">Hello, Algo!</h2>
<h2 style="margin-top: 0.8em; margin-bottom: 0.8em;">Hello, Algorithms!</h2>
</div>
<p>The advent of computers has brought significant changes to the world. With their high-speed computing power and excellent programmability, they have become the ideal medium for executing algorithms and processing data. Whether it's the realistic graphics of video games, the intelligent decisions in autonomous driving, the brilliant Go games of AlphaGo, or the natural interactions of ChatGPT, these applications are all exquisite demonstrations of algorithms at work on computers.</p>
<p>In fact, before the advent of computers, algorithms and data structures already existed in every corner of the world. Early algorithms were relatively simple, such as ancient counting methods and tool-making procedures. As civilization progressed, algorithms became more refined and complex. From the exquisite craftsmanship of artisans, to industrial products that liberate productive forces, to the scientific laws governing the universe, almost every ordinary or astonishing thing has behind it the ingenious thought of algorithms.</p>
<p>Similarly, data structures are everywhere: from social networks to subway lines, many systems can be modeled as "graphs"; from a country to a family, the main forms of social organization exhibit characteristics of "trees"; winter clothes are like a "stack", where the first item worn is the last to be taken off; a badminton shuttle tube resembles a "queue", with one end for insertion and the other for retrieval; a dictionary is like a "hash table", enabling quick search for target entries.</p>
<p>This book aims to help readers understand the core concepts of algorithms and data structures through clear, easy-to-understand animated illustrations and runnable code examples, and to be able to implement them through programming. On this basis, this book strives to reveal the vivid manifestations of algorithms in the complex world, showcasing the beauty of algorithms. I hope this book can help you!</p>
<p>The advent of computers has brought tremendous changes to the world. With their high-speed computing capabilities and excellent programmability, they have become the ideal medium for executing algorithms and processing data. Whether it's the realistic graphics in video games, the intelligent decision-making in autonomous driving, AlphaGo's brilliant Go matches, or ChatGPT's natural interactions, these applications are all exquisite interpretations of algorithms on computers.</p>
<p>In fact, before the advent of computers, algorithms and data structures already existed in every corner of the world. Early algorithms were relatively simple, such as ancient counting methods and tool-making procedures. As civilization progressed, algorithms gradually became more refined and complex. From the ingenious craftsmanship of master artisans, to industrial products that liberate productive forces, to the scientific laws governing the operation of the universe, behind almost every ordinary or astonishing thing lies ingenious algorithmic thinking.</p>
<p>Similarly, data structures are everywhere: from large-scale social networks to small subway systems, many systems can be modeled as "graphs"; from a nation to a family, the primary organizational forms of society exhibit characteristics of "trees"; winter clothing is like a "stack," where the first item put on is the last to be taken off; a badminton tube is like a "queue," with items inserted at one end and retrieved from the other; a dictionary is like a "hash table," enabling quick lookup of target entries.</p>
<p>This book aims to help readers understand the core concepts of algorithms and data structures through clear and accessible animated illustrations and runnable code examples, and to implement them through programming. Building on this foundation, the book endeavors to reveal the vivid manifestations of algorithms in the complex world and showcase the beauty of algorithms. I hope this book can be of help to you!</p>
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