Data Structure and Algorithms
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  • Binary Search
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    • Find Minimum in Rotated Sorted Array
    • Find Minimum in Rotated Sorted Array II
    • Search in Rotated Sorted Array
    • Search in Rotated Sorted Array II
    • Find Peak Element*
    • Recover Rotated Sorted Array
    • Rotate String
    • Wood Cut
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    • Closest Number in Sorted Array
    • K Closest Number in Sorted Array
    • Maximum Number in Mountain Sequence
    • Search Insert Position *
    • Pow(x, n)
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  • Graph
    • Clone Graph
    • Topological Sorting
    • Permutations
    • Permutations II
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    • Subsets II
    • Word Ladder
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    • Connected Component in Undirected Graph
    • Six Degrees
    • String Permutation II
    • Letter Case Permutation
  • Data Structure
    • Min Stack
    • Implement a Queue by Two Stacks
    • Largest Rectangle in Histogram
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    • Rehashing
    • LRU Cache
    • Subarray Sum
    • Anagrams
    • Longest Consecutive Sequence
    • Data Stream Median
    • Heapify
    • Ugly Number
    • Ugly Number II
  • Misc
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    • Fibonacci
  • Array and Numbers
    • Merge Sorted Array
    • Merge Two Sorted Arrays
    • Median of two Sorted Arrays
    • Best Time to Buy and Sell Stock
    • Best Time to Buy and Sell Stock II
    • Best Time to Buy and Sell Stock III
    • Maximum Subarray
    • Maximum Subarray II
    • Maximum Subarray III
    • Minimum Subarray
    • Maximum Subarray Difference
    • Subarray Sum
    • Subarray Sum Closest
    • Two Sum
    • 3Sum
    • 3Sum Closest
    • 4Sum
    • k Sum
    • k Sum II
    • Partition Array
    • Sort Letters by Case
    • Sort Colors
    • Sort Colors II
    • Interleaving Positive and Negative Numbers
    • Spiral Matrix
    • Spiral Matrix II
    • Rotate Image
  • Dynamic Programming I
    • Triangle
    • Minimum Path Sum
    • Unique Paths
    • Unique Paths II
    • Climbing Stairs
    • Jump Game
    • Jump Game II
    • 01 Matrix
    • Longest Line of Consecutive One in Matrix
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  • Dynamic Programming II
    • Word Break
    • Longest Common Subsequence
    • Longest Common Substring
    • Edit Distance
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    • Interleaving String
    • k Sum
  • Binary Tree And Divide Conquer
    • Binary Tree Preorder Traversal*
    • Binary Tree Inorder Traversal*
    • Binary Tree Postorder Traversal*
    • Maximum Depth of Binary Tree
    • Minimum Depth of Binary Tree
    • Balanced Binary Tree
    • Lowest Common Ancestor
    • Binary Tree Maximum Path Sum
    • Binary Tree Maximum Path Sum II
    • Binary Tree Level Order Traversal*
    • Binary Tree Level Order Traversal II
    • Binary Tree Zigzag Level Order Traversal
    • Validate Binary Search Tree
    • Inorder Successor in Binary Search Tree
    • Binary Search Tree Iterator
    • Search Range in Binary Search Tree
    • Insert Node in a Binary Search Tree
    • Remove Node in Binary Search Tree
    • Find the kth largest element in the BST
    • Kth Smallest Element in a BST
    • Serialize and Deserialize Binary Tree*
    • Construct Binary Tree from Preorder and Inorder Traversal
    • Convert Sorted Array to Binary Search Tree
    • Unique Binary Search Trees *
    • Unique Binary Search Trees II *
    • Recover Binary Search Tree
    • Same Tree
    • Symmetric Tree
    • Path Sum*
    • Path Sum II*
    • Flatten Binary Tree to Linked List
    • Populating Next Right Pointers in Each Node
    • Sum Root to Leaf Numbers
    • Binary Tree Right Side View
    • Count Complete Tree Nodes
    • Invert Binary Tree
    • Binary Tree Paths*
    • Subtree of Another Tree
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  1. Binary Search

Find Minimum in Rotated Sorted Array II

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).

Find the minimum element.

Example

Given [4,4,5,6,7,0,1,2] return 0.

Solution

//  这道题目在面试中不会让写完整的程序

//  只需要知道最坏情况下 \[1,1,1....,1\] 里有一个0

//  这种情况使得时间复杂度必须是 O\(n\)

//  因此写一个for循环就好了。

//  如果你觉得,不是每个情况都是最坏情况,你想用二分法解决不是最坏情况的情况,那你就写一个二分吧。

//  反正面试考的不是你在这个题上会不会用二分法。这个题的考点是你想不想得到最坏情况。



重复元素出现在pivot 点时候需要考虑动态的删除最后一个元素。

end = end - 1
class Solution:
    # @param num: a rotated sorted array
    # @return: the minimum number in the array
    def findMin(self, num):
        # write your code here
        start, end = 0, len(num) - 1
        while start + 1 < end:
            mid = (start+end)/2
            if num[mid] > num[end]:
                start = mid
            elif num[mid] < num[end]:
                end = mid
            else:
                end = end - 1
        return min(num[start], num[end])
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