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Chapter 0. Preface
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Chapter 0. Preface
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0.1 About this book
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Chapter 1. Encounter with algorithms
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1.1 Algorithms are everywhere
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1.2 What is an algorithm
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Chapter 2. Complexity analysis
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Chapter 2. Complexity analysis
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2.1 Algorithm efficiency assessment
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2.2 Iteration and recursion
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2.3 Time complexity
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2.4 Space complexity
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2.5 Summary
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Chapter 3. Data structures
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Chapter 3. Data structures
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3.1 Classification of data structures
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3.2 Basic data types
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3.3 Number encoding *
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3.4 Character encoding *
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3.5 Summary
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Chapter 4. Array and linked list
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Chapter 4. Array and linked list
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4.1 Array
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4.2 Linked list
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4.3 List
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4.4 Memory and cache *
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4.5 Summary
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Chapter 5. Stack and queue
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Chapter 5. Stack and queue
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5.1 Stack
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5.2 Queue
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5.3 Double-ended queue
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5.4 Summary
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Chapter 6. Hash table
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Chapter 6. Hash table
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6.1 Hash table
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6.2 Hash collision
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6.3 Hash algorithm
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6.4 Summary
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Chapter 7. Tree
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Chapter 7. Tree
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< a href = "../../chapter_tree/binary_tree/" class = "md-nav__link" >
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7.1 Binary tree
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7.2 Binary tree traversal
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7.3 Array Representation of tree
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7.4 Binary Search tree
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< a href = "../../chapter_tree/avl_tree/" class = "md-nav__link" >
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7.5 AVL tree *
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7.6 Summary
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< a href = "../../chapter_heap/" class = "md-nav__link " >
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Chapter 8. Heap
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Chapter 8. Heap
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8.1 Heap
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8.2 Building a heap
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8.3 Top-k problem
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8.4 Summary
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< a href = "../../chapter_graph/" class = "md-nav__link " >
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Chapter 9. Graph
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Chapter 9. Graph
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9.1 Graph
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9.2 Basic graph operations
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9.3 Graph traversal
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9.4 Summary
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< div class = "md-nav__link md-nav__container" >
< a href = "../../chapter_searching/" class = "md-nav__link " >
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Chapter 10. Searching
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Chapter 10. Searching
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< a href = "../../chapter_searching/binary_search/" class = "md-nav__link" >
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10.1 Binary search
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10.2 Binary search insertion
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< a href = "../../chapter_searching/binary_search_edge/" class = "md-nav__link" >
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10.3 Binary search boundaries
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< a href = "../../chapter_searching/replace_linear_by_hashing/" class = "md-nav__link" >
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10.4 Hashing optimization strategies
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< a href = "../../chapter_searching/searching_algorithm_revisited/" class = "md-nav__link" >
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10.5 Search algorithms revisited
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10.6 Summary
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< div class = "md-nav__link md-nav__container" >
< a href = "../../chapter_sorting/" class = "md-nav__link " >
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Chapter 11. Sorting
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Chapter 11. Sorting
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11.1 Sorting algorithms
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11.2 Selection sort
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11.3 Bubble sort
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11.4 Insertion sort
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11.5 Quick sort
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11.6 Merge sort
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11.7 Heap sort
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11.8 Bucket sort
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11.9 Counting sort
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11.10 Radix sort
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11.11 Summary
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< a href = "../../chapter_divide_and_conquer/" class = "md-nav__link " >
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Chapter 12. Divide and conquer
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Chapter 12. Divide and conquer
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12.1 Divide and conquer algorithms
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< a href = "../../chapter_divide_and_conquer/binary_search_recur/" class = "md-nav__link" >
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12.2 Divide and conquer search strategy
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12.3 Building binary tree problem
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< a href = "../../chapter_divide_and_conquer/hanota_problem/" class = "md-nav__link" >
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12.4 Tower of Hanoi Problem
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12.5 Summary
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13.1 Backtracking algorithms
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13.4 n queens problem
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15.1 Greedy algorithms
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< h1 id = "11-algorithms-are-everywhere" > 1.1 Algorithms are everywhere< a class = "headerlink" href = "#11-algorithms-are-everywhere" title = "Permanent link" > ¶ < / a > < / h1 >
< p > When we hear the word "algorithm," we naturally think of mathematics. However, many algorithms do not involve complex mathematics but rely more on basic logic, which can be seen everywhere in our daily lives.< / p >
< p > Before formally discussing algorithms, there's an interesting fact worth sharing: < strong > you have already unconsciously learned many algorithms and have become accustomed to applying them in your daily life< / strong > . Here, I will give a few specific examples to prove this point.< / p >
< p > < strong > Example 1: Looking Up a Dictionary< / strong > . In an English dictionary, words are listed alphabetically. Suppose we're searching for a word that starts with the letter < span class = "arithmatex" > \(r\)< / span > . This is typically done in the following way:< / p >
< ol >
< li > Open the dictionary to about halfway and check the first letter on the page, let's say the letter is < span class = "arithmatex" > \(m\)< / span > .< / li >
< li > Since < span class = "arithmatex" > \(r\)< / span > comes after < span class = "arithmatex" > \(m\)< / span > in the alphabet, we can ignore the first half of the dictionary and focus on the latter half.< / li >
< li > Repeat steps < code > 1.< / code > and < code > 2.< / code > until you find the page where the word starts with < span class = "arithmatex" > \(r\)< / span > .< / li >
< / ol >
< div class = "tabbed-set tabbed-alternate" data-tabs = "1:5" > < input checked = "checked" id = "__tabbed_1_1" name = "__tabbed_1" type = "radio" / > < input id = "__tabbed_1_2" name = "__tabbed_1" type = "radio" / > < input id = "__tabbed_1_3" name = "__tabbed_1" type = "radio" / > < input id = "__tabbed_1_4" name = "__tabbed_1" type = "radio" / > < input id = "__tabbed_1_5" name = "__tabbed_1" type = "radio" / > < div class = "tabbed-labels" > < label for = "__tabbed_1_1" > < 1> < / label > < label for = "__tabbed_1_2" > < 2> < / label > < label for = "__tabbed_1_3" > < 3> < / label > < label for = "__tabbed_1_4" > < 4> < / label > < label for = "__tabbed_1_5" > < 5> < / label > < / div >
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< p > < a class = "glightbox" href = "../algorithms_are_everywhere.assets/binary_search_dictionary_step1.png" data-type = "image" data-width = "100%" data-height = "auto" data-desc-position = "bottom" > < img alt = "Process of Looking Up a Dictionary" class = "animation-figure" src = "../algorithms_are_everywhere.assets/binary_search_dictionary_step1.png" / > < / a > < / p >
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< p > < a class = "glightbox" href = "../algorithms_are_everywhere.assets/binary_search_dictionary_step5.png" data-type = "image" data-width = "100%" data-height = "auto" data-desc-position = "bottom" > < img alt = "Binary Search in Dictionary Step 5" class = "animation-figure" src = "../algorithms_are_everywhere.assets/binary_search_dictionary_step5.png" / > < / a > < / p >
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< p align = "center" > Figure 1-1 Process of Looking Up a Dictionary < / p >
< p > This essential skill for elementary students, looking up a dictionary, is actually the famous "Binary Search" algorithm. From a data structure perspective, we can consider the dictionary as a sorted "array"; from an algorithmic perspective, the series of actions taken to look up a word in the dictionary can be viewed as "Binary Search."< / p >
< p > < strong > Example 2: Organizing Playing Cards< / strong > . When playing cards, we need to arrange the cards in our hand in ascending order, as shown in the following process.< / p >
< ol >
< li > Divide the playing cards into "ordered" and "unordered" sections, assuming initially the leftmost card is already in order.< / li >
< li > Take out a card from the unordered section and insert it into the correct position in the ordered section; after this, the leftmost two cards are in order.< / li >
< li > Continue to repeat step < code > 2.< / code > until all cards are in order.< / li >
< / ol >
< p > < a class = "glightbox" href = "../algorithms_are_everywhere.assets/playing_cards_sorting.png" data-type = "image" data-width = "100%" data-height = "auto" data-desc-position = "bottom" > < img alt = "Playing cards sorting process" class = "animation-figure" src = "../algorithms_are_everywhere.assets/playing_cards_sorting.png" / > < / a > < / p >
< p align = "center" > Figure 1-2 Playing cards sorting process < / p >
< p > The above method of organizing playing cards is essentially the "Insertion Sort" algorithm, which is very efficient for small datasets. Many programming languages' sorting functions include the insertion sort.< / p >
< p > < strong > Example 3: Making Change< / strong > . Suppose we buy goods worth < span class = "arithmatex" > \(69\)< / span > yuan at a supermarket and give the cashier < span class = "arithmatex" > \(100\)< / span > yuan, then the cashier needs to give us < span class = "arithmatex" > \(31\)< / span > yuan in change. They would naturally complete the thought process as shown in Figure 1-3.< / p >
< ol >
< li > The options are currencies smaller than < span class = "arithmatex" > \(31\)< / span > , including < span class = "arithmatex" > \(1\)< / span > , < span class = "arithmatex" > \(5\)< / span > , < span class = "arithmatex" > \(10\)< / span > , and < span class = "arithmatex" > \(20\)< / span > .< / li >
< li > Take out the largest < span class = "arithmatex" > \(20\)< / span > from the options, leaving < span class = "arithmatex" > \(31 - 20 = 11\)< / span > .< / li >
< li > Take out the largest < span class = "arithmatex" > \(10\)< / span > from the remaining options, leaving < span class = "arithmatex" > \(11 - 10 = 1\)< / span > .< / li >
< li > Take out the largest < span class = "arithmatex" > \(1\)< / span > from the remaining options, leaving < span class = "arithmatex" > \(1 - 1 = 0\)< / span > .< / li >
< li > Complete the change-making, with the solution being < span class = "arithmatex" > \(20 + 10 + 1 = 31\)< / span > .< / li >
< / ol >
< p > < a class = "glightbox" href = "../algorithms_are_everywhere.assets/greedy_change.png" data-type = "image" data-width = "100%" data-height = "auto" data-desc-position = "bottom" > < img alt = "Change making process" class = "animation-figure" src = "../algorithms_are_everywhere.assets/greedy_change.png" / > < / a > < / p >
< p align = "center" > Figure 1-3 Change making process < / p >
< p > In the above steps, we make the best choice at each step (using the largest denomination possible), ultimately resulting in a feasible change-making plan. From the perspective of data structures and algorithms, this method is essentially a "Greedy" algorithm.< / p >
< p > From cooking a meal to interstellar travel, almost all problem-solving involves algorithms. The advent of computers allows us to store data structures in memory and write code to call the CPU and GPU to execute algorithms. In this way, we can transfer real-life problems to computers, solving various complex issues more efficiently.< / p >
< div class = "admonition tip" >
< p class = "admonition-title" > Tip< / p >
< p > If concepts such as data structures, algorithms, arrays, and binary search still seem somewhat obsecure, I encourage you to continue reading. This book will gently guide you into the realm of understanding data structures and algorithms.< / p >
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