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Preface
- FAQ
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Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
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Part II - Coding
- String
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Integer Array
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Remove Element
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Zero Sum Subarray
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Subarray Sum K
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Subarray Sum Closest
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Recover Rotated Sorted Array
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Product of Array Exclude Itself
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Partition Array
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First Missing Positive
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2 Sum
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3 Sum
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3 Sum Closest
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Remove Duplicates from Sorted Array
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Remove Duplicates from Sorted Array II
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Merge Sorted Array
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Merge Sorted Array II
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Median
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Partition Array by Odd and Even
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Kth Largest Element
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Remove Element
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Binary Search
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First Position of Target
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Search Insert Position
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Search for a Range
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First Bad Version
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Search a 2D Matrix
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Search a 2D Matrix II
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Find Peak Element
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Search in Rotated Sorted Array
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Search in Rotated Sorted Array II
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Find Minimum in Rotated Sorted Array
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Find Minimum in Rotated Sorted Array II
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Median of two Sorted Arrays
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Sqrt x
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Wood Cut
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First Position of Target
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Math and Bit Manipulation
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Single Number
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Single Number II
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Single Number III
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O1 Check Power of 2
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Convert Integer A to Integer B
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Factorial Trailing Zeroes
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Unique Binary Search Trees
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Update Bits
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Fast Power
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Hash Function
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Happy Number
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Count 1 in Binary
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Fibonacci
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A plus B Problem
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Print Numbers by Recursion
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Majority Number
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Majority Number II
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Majority Number III
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Digit Counts
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Ugly Number
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Plus One
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Palindrome Number
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Task Scheduler
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Single Number
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Linked List
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Remove Duplicates from Sorted List
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Remove Duplicates from Sorted List II
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Remove Duplicates from Unsorted List
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Partition List
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Add Two Numbers
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Two Lists Sum Advanced
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Remove Nth Node From End of List
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Linked List Cycle
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Linked List Cycle II
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Reverse Linked List
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Reverse Linked List II
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Merge Two Sorted Lists
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Merge k Sorted Lists
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Reorder List
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Copy List with Random Pointer
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Sort List
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Insertion Sort List
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Palindrome Linked List
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LRU Cache
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Rotate List
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Swap Nodes in Pairs
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Remove Linked List Elements
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Remove Duplicates from Sorted List
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Binary Tree
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Binary Tree Preorder Traversal
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Binary Tree Inorder Traversal
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Binary Tree Postorder Traversal
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Binary Tree Level Order Traversal
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Binary Tree Level Order Traversal II
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Maximum Depth of Binary Tree
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Balanced Binary Tree
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Binary Tree Maximum Path Sum
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Lowest Common Ancestor
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Invert Binary Tree
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Diameter of a Binary Tree
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Construct Binary Tree from Preorder and Inorder Traversal
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Construct Binary Tree from Inorder and Postorder Traversal
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Subtree
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Binary Tree Zigzag Level Order Traversal
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Binary Tree Serialization
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Binary Tree Preorder Traversal
- Binary Search Tree
- Exhaustive Search
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Dynamic Programming
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Triangle
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Backpack
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Backpack II
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Minimum Path Sum
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Unique Paths
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Unique Paths II
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Climbing Stairs
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Jump Game
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Word Break
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Longest Increasing Subsequence
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Palindrome Partitioning II
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Longest Common Subsequence
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Edit Distance
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Jump Game II
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Best Time to Buy and Sell Stock
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Best Time to Buy and Sell Stock II
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Best Time to Buy and Sell Stock III
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Best Time to Buy and Sell Stock IV
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Distinct Subsequences
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Interleaving String
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Maximum Subarray
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Maximum Subarray II
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Longest Increasing Continuous subsequence
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Longest Increasing Continuous subsequence II
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Maximal Square
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Triangle
- Graph
- Data Structure
- Big Data
- Problem Misc
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Part III - Contest
- Google APAC
- Microsoft
- Appendix I Interview and Resume
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Tags
Insertion Sort - 插入排序
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))
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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();
}
}
}
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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;
}
}
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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;
}
}
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