162. Find Peak Element
Description
A peak element is an element that is strictly greater than its neighbors.
Given a 0-indexed integer array nums
, find a peak element, and return its index. If the array contains multiple peaks, return the index to any of the peaks.
You may imagine that nums[-1] = nums[n] = -∞
. In other words, an element is always considered to be strictly greater than a neighbor that is outside the array.
You must write an algorithm that runs in O(log n)
time.
Example 1:
Input: nums = [1,2,3,1] Output: 2 Explanation: 3 is a peak element and your function should return the index number 2.
Example 2:
Input: nums = [1,2,1,3,5,6,4] Output: 5 Explanation: Your function can return either index number 1 where the peak element is 2, or index number 5 where the peak element is 6.
Constraints:
1 <= nums.length <= 1000
-231 <= nums[i] <= 231 - 1
nums[i] != nums[i + 1]
for all validi
.
Solutions
Solution 1: Binary Search
We define the left boundary of binary search as $left=0$ and the right boundary as $right=n-1$, where $n$ is the length of the array. In each step of binary search, we find the middle element $mid$ of the current interval, and compare the values of $mid$ and its right neighbor $mid+1$:
- If the value of $mid$ is greater than the value of $mid+1$, there exists a peak element on the left side, and we update the right boundary $right$ to $mid$.
- Otherwise, there exists a peak element on the right side, and we update the left boundary $left$ to $mid+1$.
- Finally, when the left boundary $left$ is equal to the right boundary $right$, we have found the peak element of the array.
The time complexity is $O(\log n)$, where $n$ is the length of the array $nums$. Each step of binary search can reduce the search interval by half, so the time complexity is $O(\log n)$. The space complexity is $O(1)$.
1 2 3 4 5 6 7 8 9 10 |
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 |
|
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 |
|
1 2 3 4 5 6 7 8 9 10 11 12 |
|
1 2 3 4 5 6 7 8 9 10 11 12 |
|