- Preface
- FAQ
- Guidelines for Contributing
- Contributors
- Part I - Basics
- Basics Data Structure
- String
- Linked List
- Binary Tree
- Huffman Compression
- Queue
- Heap
- Stack
- Set
- Map
- Graph
- Basics Sorting
- 算法复习——排序
- Bubble Sort
- Selection Sort
- Insertion Sort
- Merge Sort
- Quick Sort
- Heap Sort
- Bucket Sort
- Counting Sort
- Radix Sort
- Basics Algorithm
- Divide and Conquer
- Binary Search
- Math
- Greatest Common Divisor
- Prime
- Knapsack
- Probability
- Shuffle
- Bitmap
- Basics Misc
- Bit Manipulation
- Part II - Coding
- String
- strStr
- Two Strings Are Anagrams
- Compare Strings
- Anagrams
- Longest Common Substring
- Rotate String
- Reverse Words in a String
- Valid Palindrome
- Longest Palindromic Substring
- Space Replacement
- Wildcard Matching
- Length of Last Word
- Count and Say
- Integer Array
- Remove Element
- Zero Sum Subarray
- Subarray Sum K
- Subarray Sum Closest
- Recover Rotated Sorted Array
- Product of Array Exclude Itself
- Partition Array
- First Missing Positive
- 2 Sum
- 3 Sum
- 3 Sum Closest
- Remove Duplicates from Sorted Array
- Remove Duplicates from Sorted Array II
- Merge Sorted Array
- Merge Sorted Array II
- Median
- Partition Array by Odd and Even
- Kth Largest Element
- Binary Search
- Binary Search
- Search Insert Position
- Search for a Range
- First Bad Version
- Search a 2D Matrix
- Search a 2D Matrix II
- Find Peak Element
- Search in Rotated Sorted Array
- Search in Rotated Sorted Array II
- Find Minimum in Rotated Sorted Array
- Find Minimum in Rotated Sorted Array II
- Median of two Sorted Arrays
- Sqrt x
- Wood Cut
- Math and Bit Manipulation
- Single Number
- Single Number II
- Single Number III
- O1 Check Power of 2
- Convert Integer A to Integer B
- Factorial Trailing Zeroes
- Unique Binary Search Trees
- Update Bits
- Fast Power
- Hash Function
- Count 1 in Binary
- Fibonacci
- A plus B Problem
- Print Numbers by Recursion
- Majority Number
- Majority Number II
- Majority Number III
- Digit Counts
- Ugly Number
- Plus One
- Linked List
- Remove Duplicates from Sorted List
- Remove Duplicates from Sorted List II
- Remove Duplicates from Unsorted List
- Partition List
- Add Two Numbers
- Two Lists Sum Advanced
- Remove Nth Node From End of List
- Linked List Cycle
- Linked List Cycle II
- Reverse Linked List
- Reverse Linked List II
- Merge Two Sorted Lists
- Merge k Sorted Lists
- Reorder List
- Copy List with Random Pointer
- Sort List
- Insertion Sort List
- Palindrome Linked List
- Delete Node in the Middle of Singly Linked List
- Rotate List
- Swap Nodes in Pairs
- Remove Linked List Elements
- Binary Tree
- Binary Tree Preorder Traversal
- Binary Tree Inorder Traversal
- Binary Tree Postorder Traversal
- Binary Tree Level Order Traversal
- Binary Tree Level Order Traversal II
- Maximum Depth of Binary Tree
- Balanced Binary Tree
- Binary Tree Maximum Path Sum
- Lowest Common Ancestor
- Invert Binary Tree
- Diameter of a Binary Tree
- Construct Binary Tree from Preorder and Inorder Traversal
- Construct Binary Tree from Inorder and Postorder Traversal
- Subtree
- Binary Tree Zigzag Level Order Traversal
- Binary Tree Serialization
- Binary Search Tree
- Insert Node in a Binary Search Tree
- Validate Binary Search Tree
- Search Range in Binary Search Tree
- Convert Sorted Array to Binary Search Tree
- Convert Sorted List to Binary Search Tree
- Binary Search Tree Iterator
- Exhaustive Search
- Subsets
- Unique Subsets
- Permutations
- Unique Permutations
- Next Permutation
- Previous Permuation
- Permutation Index
- Permutation Index II
- Permutation Sequence
- Unique Binary Search Trees II
- Palindrome Partitioning
- Combinations
- Combination Sum
- Combination Sum II
- Minimum Depth of Binary Tree
- Word Search
- Dynamic Programming
- Triangle
- Backpack
- Backpack II
- Minimum Path Sum
- Unique Paths
- Unique Paths II
- Climbing Stairs
- Jump Game
- Word Break
- Longest Increasing Subsequence
- Follow up
- Palindrome Partitioning II
- Longest Common Subsequence
- Edit Distance
- Jump Game II
- Best Time to Buy and Sell Stock
- Best Time to Buy and Sell Stock II
- Best Time to Buy and Sell Stock III
- Best Time to Buy and Sell Stock IV
- Distinct Subsequences
- Interleaving String
- Maximum Subarray
- Maximum Subarray II
- Longest Increasing Continuous subsequence
- Longest Increasing Continuous subsequence II
- Maximal Square
- Graph
- Find the Connected Component in the Undirected Graph
- Route Between Two Nodes in Graph
- Topological Sorting
- Word Ladder
- Bipartial Graph Part I
- Data Structure
- Implement Queue by Two Stacks
- Min Stack
- Sliding Window Maximum
- Longest Words
- Heapify
- Problem Misc
- Nuts and Bolts Problem
- String to Integer
- Insert Interval
- Merge Intervals
- Minimum Subarray
- Matrix Zigzag Traversal
- Valid Sudoku
- Add Binary
- Reverse Integer
- Gray Code
- Find the Missing Number
- Minimum Window Substring
- Continuous Subarray Sum
- Continuous Subarray Sum II
- Longest Consecutive Sequence
- Part III - Contest
- Google APAC
- APAC 2015 Round B
- Problem A. Password Attacker
- APAC 2016 Round D
- Problem A. Dynamic Grid
- Microsoft
- Microsoft 2015 April
- Problem A. Magic Box
- Problem B. Professor Q's Software
- Problem C. Islands Travel
- Problem D. Recruitment
- Microsoft 2015 April 2
- Problem A. Lucky Substrings
- Problem B. Numeric Keypad
- Problem C. Spring Outing
- Microsoft 2015 September 2
- Problem A. Farthest Point
- Appendix I Interview and Resume
- Interview
- Resume
- 術語表
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Insertion Sort
核心:通过构建有序序列,对于未排序序列,在已排序序列中从后向前扫描(对于单向链表则只能从前往后遍历),找到相应位置并插入。实现上通常使用 in-place 排序(需用到 O(1) 的额外空间)
- 从第一个元素开始,该元素可认为已排序
- 取下一个元素,对已排序数组从后往前扫描
- 若从排序数组中取出的元素大于新元素,则移至下一位置
- 重复步骤 3,直至找到已排序元素小于或等于新元素的位置
- 插入新元素至该位置
- 重复 2~5
性质:
- 交换操作和数组中倒置的数量相同
- 比较次数>=倒置数量,<=倒置的数量加上数组的大小减一
- 每次交换都改变了两个顺序颠倒的元素的位置,即减少了一对倒置,倒置数量为 0 时即完成排序。
- 每次交换对应着一次比较,且 1 到 N-1 之间的每个 i 都可能需要一次额外的记录(a[i]未到达数组左端时)
- 最坏情况下需要~N^2/2 次比较和~N^2/2 次交换,最好情况下需要 N-1 次比较和 0 次交换。
- 平均情况下需要~N^2/4 次比较和~N^2/4 次交换
Implementation
Python
#!/usr/bin/env python
def insertionSort(alist):
for i, item_i in enumerate(alist):
print alist
index = i
while index > 0 and alist[index - 1] > item_i:
alist[index] = alist[index - 1]
index -= 1
alist[index] = item_i
return alist
unsorted_list = [6, 5, 3, 1, 8, 7, 2, 4]
print(insertionSort(unsorted_list))
Java
public class Sort {
public static void main(String[] args) {
int unsortedArray[] = new int[]{6, 5, 3, 1, 8, 7, 2, 4};
insertionSort(unsortedArray);
System.out.println("After sort: ");
for (int item : unsortedArray) {
System.out.print(item + " ");
}
}
public static void insertionSort(int[] array) {
int len = array.length;
for (int i = 0; i < len; i++) {
int index = i, array_i = array[i];
while (index > 0 && array[index - 1] > array_i) {
array[index] = array[index - 1];
index -= 1;
}
array[index] = array_i;
/* print sort process */
for (int item : array) {
System.out.print(item + " ");
}
System.out.println();
}
}
}
实现(C++):
template<typename T>
void insertion_sort(T arr[], int len) {
int i, j;
T temp;
for (int i = 1; i < len; i++) {
temp = arr[i];
for (int j = i - 1; j >= 0 && arr[j] > temp; j--) {
a[j + 1] = a[j];
}
arr[j + 1] = temp;
}
}
希尔排序
核心:基于插入排序,使数组中任意间隔为 h 的元素都是有序的,即将全部元素分为 h 个区域使用插入排序。其实现可类似于插入排序但使用不同增量。更高效的原因是它权衡了子数组的规模和有序性。
实现(C++):
template<typename T>
void shell_sort(T arr[], int len) {
int gap, i, j;
T temp;
for (gap = len >> 1; gap > 0; gap >>= 1)
for (i = gap; i < len; i++) {
temp = arr[i];
for (j = i - gap; j >= 0 && arr[j] > temp; j -= gap)
arr[j + gap] = arr[j];
arr[j + gap] = temp;
}
}
Reference
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