-
Preface
- FAQ
-
Part I - Basics
- Basics Data Structure
- Basics Sorting
- Basics Algorithm
- Basics Misc
-
Part II - Coding
- String
-
Integer Array
-
Remove Element
-
Zero Sum Subarray
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Subarray Sum K
-
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
-
Partition Array by Odd and Even
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Kth Largest Element
-
Remove Element
-
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
-
Plus One
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Palindrome Number
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Task Scheduler
-
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
-
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
-
Triangle
-
Backpack
-
Backpack II
-
Minimum Path Sum
-
Unique Paths
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Unique Paths II
-
Climbing Stairs
-
Jump Game
-
Word Break
-
Longest Increasing Subsequence
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Palindrome Partitioning II
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Longest Common Subsequence
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Edit Distance
-
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
-
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
-
Tags
Search in Rotated Sorted Array
Problem
Metadata
- tags: Binary Search, LinkedIn, Array, Facebook, Sorted Array, Uber, Microsoft, Bloomberg
- difficulty: Medium
- source(leetcode): https://leetcode.com/problems/search-in-rotated-sorted-array/
- source(lintcode): https://www.lintcode.com/problem/search-in-rotated-sorted-array/
Description
Suppose a sorted array is rotated at some pivot unknown to you beforehand.
(i.e., 0 1 2 4 5 6 7
might become 4 5 6 7 0 1 2
).
You are given a target value to search. If found in the array return its index, otherwise return -1.
You may assume no duplicate exists in the array.
Example
For [4, 5, 1, 2, 3]
and target=1
, return 2
.
For [4, 5, 1, 2, 3]
and target=0
, return -1
.
Challenge
O(logN) time
题解1 - 找到有序数组
对于旋转数组的分析可使用画图的方法,如下图所示,升序数组经旋转后可能为如下两种形式。
对于有序数组,使用二分搜索比较方便。分析题中的数组特点,旋转后初看是乱序数组,但仔细一看其实里面是存在两段有序数组的。刚开始做这道题时可能会去比较target
和A[mid]
, 但分析起来异常复杂。该题较为巧妙的地方在于如何找出旋转数组中的局部有序数组,并使用二分搜索解之。结合实际数组在纸上分析较为方便。
C++
/**
* 本代码fork自
* http://www.jiuzhang.com/solutions/search-in-rotated-sorted-array/
*/
class Solution {
/**
* param A : an integer ratated sorted array
* param target : an integer to be searched
* return : an integer
*/
public:
int search(vector<int> &A, int target) {
if (A.empty()) {
return -1;
}
vector<int>::size_type start = 0;
vector<int>::size_type end = A.size() - 1;
vector<int>::size_type mid;
while (start + 1 < end) {
mid = start + (end - start) / 2;
if (target == A[mid]) {
return mid;
}
if (A[start] < A[mid]) {
// situation 1, numbers between start and mid are sorted
if (A[start] <= target && target < A[mid]) {
end = mid;
} else {
start = mid;
}
} else {
// situation 2, numbers between mid and end are sorted
if (A[mid] < target && target <= A[end]) {
start = mid;
} else {
end = mid;
}
}
}
if (A[start] == target) {
return start;
}
if (A[end] == target) {
return end;
}
return -1;
}
};
copy
Java
public class Solution {
/**
*@param A : an integer rotated sorted array
*@param target : an integer to be searched
*return : an integer
*/
public int search(int[] A, int target) {
if (A == null || A.length == 0) return -1;
int lb = 0, ub = A.length - 1;
while (lb + 1 < ub) {
int mid = lb + (ub - lb) / 2;
if (A[mid] == target) return mid;
if (A[mid] > A[lb]) {
// case1: numbers between lb and mid are sorted
if (A[lb] <= target && target <= A[mid]) {
ub = mid;
} else {
lb = mid;
}
} else {
// case2: numbers between mid and ub are sorted
if (A[mid] <= target && target <= A[ub]) {
lb = mid;
} else {
ub = mid;
}
}
}
if (A[lb] == target) {
return lb;
} else if (A[ub] == target) {
return ub;
}
return -1;
}
}
copy
源码分析
- 若
target == A[mid]
,索引找到,直接返回 - 寻找局部有序数组,分析
A[mid]
和两段有序的数组特点,由于旋转后前面有序数组最小值都比后面有序数组最大值大。故若A[start] < A[mid]
成立,则start与mid间的元素必有序(要么是前一段有序数组,要么是后一段有序数组,还有可能是未旋转数组)。 - 接着在有序数组
A[start]~A[mid]
间进行二分搜索,但能在A[start]~A[mid]
间搜索的前提是A[start] <= target <= A[mid]
。 - 接着在有序数组
A[mid]~A[end]
间进行二分搜索,注意前提条件。 - 搜索完毕时索引若不是mid或者未满足while循环条件,则测试A[start]或者A[end]是否满足条件。
- 最后若未找到满足条件的索引,则返回-1.
复杂度分析
分两段二分,时间复杂度仍近似为 .
题解2 - 应用两次二分
应用两次二分搜索:第一次是找到分段点,第二次是对分段点两边的有序数组(之一)进行搜索。后者非常简单,关键是第一步怎么找分段点。
乍一看,有序数组经过旋转就不再有序、也不单调了,好像用不了二分。其实不然,分段点左边的元素全都 ≥A[0]、右边元素全都 <A[0], 这就是一个单调性质,借助这个性质就能二分地找到段点。
注:如果觉得上述“二分性质”不够显著,可以引入一个辅助数组 A'
来理解, 令 A'[i] = A[i] < A[0] ? true : false
. 比如示例中 A = [4, 5, 6, 7, 0, 1, 2]
对应的 A'
就是 [false, false, false, false, true, true, true]
. 显然 A'
是单调序列,只不过元素取值仅 true 和 false 两种。
Java
public class Solution {
/**
*@param A : an integer rotated sorted array
*@param target : an integer to be searched
*return : an integer
*/
public int search(int[] A, int target) {
if (A == null || A.length == 0) {
return -1;
}
int p = findBreakPoint(A);
if (target >= A[0]) {
// search in [lo, segPoint]
return binSearch(A, target, 0, p);
} else {
// search in [segPoint, hi]
return binSearch(A, target, p, A.length - 1);
}
}
private int findBreakPoint(int[] A) {
// A[index] < A[0], min[index]
int index;
int lo = 0, hi = A.length - 1, segValue = A[0];
while (lo + 1 < hi) {
int md = lo + (hi - lo)/2;
if (A[md] > segValue) {
lo = md;
} else {
hi = md;
}
}
index = A[lo] < segValue ? lo : hi;
return index;
}
private int binSearch(int[] A, int target, int lo, int hi) {
while (lo + 1 < hi) {
int md = lo + (hi - lo) / 2;
if (A[md] == target) {
lo = md;
} else if (A[md] < target) {
lo = md;
} else {
hi = md;
}
}
if (A[lo] == target) {
return lo;
}
if (A[hi] == target) {
return hi;
}
return -1;
}
}
copy
复杂度分析
第一次二分找段点时间复杂度为 O(log n) , 第二次在局部有序数组上二分时间复杂度不超过 O(log n) , 总起来还是近似 O(log n) .