Leetcode 34 Find First and Last Position of Element in Sorted Array 解题思路 (python)

时间:2023-03-09 19:17:47
Leetcode 34 Find First and Last Position of Element in Sorted Array 解题思路 (python)

本人编程小白,如果有写的不对、或者能更完善的地方请个位批评指正!

这个是leetcode的第34题,这道题的tag是数组,需要用到二分搜索法来解答

34. Find First and Last Position of Element in Sorted Array

Given an array of integers nums sorted in ascending order, find the starting and ending position of a given target value.

Your algorithm's runtime complexity must be in the order of O(log n).

If the target is not found in the array, return [-1, -1].

Example 1:

Input: nums = [5,7,7,8,8,10], target = 8Output: [3,4]

Example 2:

Input: nums = [5,7,7,8,8,10], target = 6Output: [-1,-1]

这道题目的难点在于O(log n),看到了O(log n)想到的基本就是二分搜索法,但怎么实现呢?大致有以下两种思路:

方法一:

思路:

先做一遍二分搜索,如果不能找到target的话返回(-1,-1)如果能找到的话然后向左、向右搜索进而找到最小和最大的target,返回index

这种解法的时间复杂度平均是O(log(n)),但当所有在数组中的数字都是一样的并且也都等于target的时候最差是O(n)

Python 代码实现:

class Solution:
def searchRange(self, A, target):
left = 0; right = len(A) - 1
while left <= right:
mid = (left + right) // 2
if A[mid] > target:
right = mid - 1
elif A[mid] < target:
left = mid + 1
else:
list = [0, 0]
if A[left] == target:
list[0] = left
if A[right] == target:
list[1] = right
for i in range(mid, right+1):
if A[i] != target:
list[1] = i - 1; break
for i in range(mid, left-1, -1):
if A[i] != target:
list[0] = i + 1; break
return list
return [-1, -1]

方法二:

思路:

做两遍二分搜索,如果不能找到target的话返回(-1,-1)如果能找到的话第一遍返回最小的index,第二遍返回最大的index,这样的话可以保证在最差的情况下时间复杂度是O(log(n))。

那么这两种二分法的搜索到底怎么实现呢?具体方法见参考文献【1】里面的2.1和2.2。

我们希望实现找到第一个与target相等的元素和最后一个与target相等的元素

(1)找到第一个与target相等的元素,如果没有的话返回-1:

def search_first_target(self, nums, target):
left,right = 0,len(nums)-1
while (left <= right):
mid = (left + right) >> 1
if (nums[mid] >= target): # 注意1
right = mid - 1
else:
left = mid + 1
if nums[left] == target:
return left # 注意2
else:
return -1

(2)找到最后一个与target相等的元素,如果没有的话返回-1:

def search_last_target(self, nums, target):
left,right = 0,len(nums)-1
while (left <= right):
mid = (left + right) >> 1
if (nums[mid] > target): # 注意1
right = mid - 1
else:
left = mid + 1
if nums[right] == target:
return right # 注意2
else:
return -1

最后的实现只是需要把以上两段代码合并到一起即可:

时间复杂度:log(n)

Python 代码实现:

 class Solution(object):
def searchRange(self, nums, target):
l,r = 0, len(nums)-1
left,right = -1,-1
result_left, result_right = -1, -1
while(l <= r):
mid = r+(l-r)//2
if (nums[mid] > target):
r = mid - 1
elif (nums[mid] < target):
l = mid + 1
elif (nums[mid] == target):
r = mid - 1
result_left = nums[mid]
if result_left == target:
left = l
else:
return -1,-1
l,r = 0, len(nums)-1
while(l <= r):
mid = r+(l-r)//2
if (nums[mid] > target):
r = mid - 1
elif(nums[mid] < target):
l = mid + 1
elif(nums[mid] == target):
l = mid + 1
result_right = nums[mid]
if result_right == target:
right = r
return left, right

参考文献:

    1.分析了二分法的不同情况,写的不错:https://www.cnblogs.com/luoxn28/p/5767571.html

     2. http://blog.csdn.net/int64ago/article/details/7425727