Diagram of algorithms

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This illustrative, illustrated book illustrates algorithms in an easy-to-understand way, designed to help programmers better harness the power of algorithms in everyday projects. The first three chapters of the book will help lay the groundwork, taking you through binary lookup, big O notation, two basic data structures, and recursion. The rest of the paper will focus on algorithms that are widely used, including techniques for solving specific problems, such as when to use greedy algorithms or dynamic programming; Hash table application; Graph algorithms; Kui nearest neighbor algorithm.

Chapter 1 Introduction to the algorithm 1 1.1 Introduction 1 1.1.1 Performance aspects 1 1.1.2 Problem Solving Techniques 2 1.2 Binary Search 2 1.2.1 Better Search methods 4 1.2.2 Runtime 8 1.3 Big O representation 8 1.3.1 The algorithm's running time increases at different rates 9 1.3.2 Understanding different big O running times 10 1.3.3 Big O notation indicates the worst case running time 12 1.3.4 Some common big O running times 12 1.3.5 Travel salesman 13 1.4 Summary 15 Chapter 2 Selecting Sort 16 2.1 How Memory works 16 2.2 Arrays and Linked Lists 18 2.2.1 Linked lists 19 2.2.2 Arrays 20 2.2.3 Terminology 21 2.2.4 Inserting 22 2.2.5 Deleting 23 2.3 Selecting Sort 25 2.4 Summary 28 Chapter 3 Recursion 29 3.1 Recursion 29 3.2 Baseline and Recursion Conditions 32 3.3 Stack 33 3.3.1 Call Stack 34 3.3.2 Recursive Call stack 36 3.4 Summary 40 Chapter 4 Quicksort 41 4.1 Divide and conquer 41 4.2 Quicksort 47 4.3 More big O notation 52 4.3.1 Comparing Merge sort and Quicksort 53 4.3.2 Average and Worst Case 54 4.4 Summary 57 Chapter 5 Hash Tables 58 5.1 Hash Functions 60 5.2 Application Cases 63 5.2.1 Using hash tables for lookups 63 5.2.2 Preventing duplicating 64 5.2.3 Using hash tables as a cache 66 5.2.4 Summary 68 5.3 Conflicts 69 5.4 Performance 71 5.4.1 Filling factors 72 5.4.2 Good hash functions 74 5.5 Summary 75 Chapter 6 Breadth-first Search 76 6.1 Overview of graphs 77 6.2 What is a graph 79 6.3 Breadth-first search 79 6.3.1 Finding the shortest path 82 6.3.2 Queue 83 6.4 Implementation Figure 84 6.5 Implementation Algorithm 86 6.6 Summary 93 Chapter 7 The Dikstra algorithm 94 7.1 Using the Dikstra algorithm 95 7.2 Terminology 98 7.3 Replacing the Piano 100 7.4 Negative edge 105 7.5 Implementation 108 7.6 Summary 116 Chapter 8 Greedy algorithm 117 8.1 Classroom scheduling problem 117 8.2 Knapsacks problem 119 8.3 Set coverage problem 121 8.4 NP complete problem 127 8.4.1 Travel salesman problem 127 8.4.2 How to Identify NP complete problem 131 8.5 Summary 133 Chapter 9 Dynamic programming 134 9.1 Knapbag problem 134 9.1.1 Simple Algorithm 135 9.1.2 Dynamic Programming 136 9.2 Knapbag Problem FAQ143 9.2.1 What will happen if one more item is added 143 9.2.2 What will happen if the order of the rows changes 145 9.2.3 Can the grid be filled column by column instead of row by row 146 9.2.4 How about adding a smaller item 146 9.2.5 Can a part of the item be stolen 146 9.2.6 Travel itinerary optimization 147 9.2.7 Handling interdependent situations 148 149 9.3 Longest common substring 149 9.3.1 Drawing a grid 150 9.3.2 Filling a grid 151 9.3.3 Reveal the answer 152 9.3.4 Longest Common Subsequence 153 9.3.5 Solution of longest Common Subsequence 154 9.4 Summary 155 Chapter 10 K nearest Neighbor algorithm 156 10.1 Orange or Grapefruit 156 10.2 Creating a recommendation system 158 10.2.1 Feature extraction 159 10.2.2 Regression 162 10.2.3 Picking the right features 164 10.3 Introduction to machine learning 165 10.3.1OCR165 10.3.2 Creating spam filters 166 10.3.3 Predicting the Stock market 167 10.4 Summary 167 Chapter 11 168 11.1 Tree 168 11.2 Reverse Index 171 11.3 Fourier Transform 171 11.4 Parallel Algorithms 172 11.5 MapReduce173 11.5.1 Why distributed Algorithms are Useful 173 11.5.2 Mapping function 173 11.5.3 Merging function 174 11.6 Bloom Filter and HyperLogLog174 11.6.1 Bloom Filter 175 11.6.2 HyperLogLog176 11.7 SHA algorithm 176 11.7.1 Comparing files 177 11.7.2 Checking passwords 178 11.8 Locally sensitive Hashes 178 11.9 Diffie-Hellman Key Exchange 179 11.10 Linear programming 180 11.11 Conclusion 180 Exercise answer 181Copy the code

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