Given an array of integers 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]
.
For example,
Given [5, 7, 7, 8, 8, 10]
and target value 8,
return [3, 4]
.
分析
思路1:
首先想到的思路是用二分法查找到target,然后从target开始,同时向左右两边扩展窗口,但是最坏的时间复杂度是O(n);
思路2:
采用递归,recursive。
终止条件:
1. 当 nums[l] == target == nums[r]
返回{l,r}
2. 当 target 落在 [nums[l],nums[r]]之外,返回{-1,-1}
递归部分:
当 nums[l] <= target <= nums[r],
分别对左半边和后半边进行search。同时将两个结果进行合并。
假设有一数组nums[],我们把它分为两部分
A...B C...D
当出现终止条件中的一条时,都会立即返回;
如果俩个部分都进入了递归部分,说明
A <= target <= B
C <= target <= D
所以可以得知,
target = B = C
那么A..B的左边界就是nums的左边界,C..D的右边界就是nums的右边界,否则返回其中一个不是{-1,-1}的结果,就是这样合并两个结果的。
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
|
class
Solution {
public
:
vector<
int
> searchRange(vector<
int
>& nums,
int
target) {
return
helper(nums, 0, nums.size() - 1, target);
}
vector<
int
> helper(vector<
int
> & nums,
int
l,
int
r,
int
target){
if
(nums.empty())
return
vector<
int
>{-1,-1};
if
(nums[l] == target && target == nums[r]){
return
vector<
int
>{l,r};
}
else
if
(nums[l] <= target && target <= nums[r]){
int
mid = (l + r) >> 1;
vector<
int
> L = helper(nums, l, mid, target);
vector<
int
> R = helper(nums, mid+1, r, target);
if
(L[0] != -1 && R[0] != -1){
return
vector<
int
> {L[0],R[1]};
}
else
if
(L[0] != -1){
return
L;
}
else
{
// 剩下的 R 不为{-1, -1} 和 R 是{-1, -1}的情况可以合并
return
R;
}
}
return
vector<
int
>{-1, -1};
}
};
|
思路3:
使用一般的二分查找,找到target的首个元素
然后再找 target+1 可以插入的首个位置,
这两者之间即为所求
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
|
class
Solution {
public
:
vector<
int
> searchRange(vector<
int
>& nums,
int
target) {
if
(nums.empty())
return
vector<
int
> {-1, -1};
int
l = bs(nums, target);
if
(nums[l] == target){
int
r = bs(nums, target + 1);
/**
* 当nums的最后一个元素是target的时候,需要将边界r自增才是可以插入target+1的位置
* 比如[2,2,2]则r的值是2,和[2,2,4]的返回值同是2,
*/
if
(nums[r] == target)
r++;
return
vector<
int
> {l, r - 1};
}
else
{
return
vector<
int
> {-1, -1};
}
}
// using binary search to find the first element in nums or the position that could be used to insert the element
int
bs(vector<
int
> & nums,
int
target){
int
l = 0, r = nums.size() - 1, mid;
while
(l < r){
mid = (l + r) >> 1;
if
(nums[mid] < target){
l = mid + 1;
}
else
{
r = mid;
}
}
return
r;
}
};
|
思路4
写两个找边界的函数,一个找上边界,一个找下边界;
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
|
class
Solution {
public
:
vector<
int
> searchRange(vector<
int
>& nums,
int
target) {
if
(nums.empty())
return
vector<
int
> {-1, -1};
int
l = lob(nums, target);
if
(nums[l] == target){
int
r = upb(nums, target);
return
vector<
int
> {l, r};
}
return
vector<
int
> {-1, -1};
}
int
lob(vector<
int
> & nums,
int
target){
int
l = 0, r = nums.size() - 1, mid;
while
(l < r){
mid = (l + r) >> 1;
// insure the mid always be at the lower position
if
(nums[mid] < target){
l = mid + 1;
}
else
{
r = mid;
}
}
return
r;
}
int
upb(vector<
int
> & nums,
int
target){
int
l = 0, r = nums.size() - 1, mid;
while
(l < r){
mid = (l + r + 1) >> 1;
// insure the mid always be at the upper position
if
(nums[mid] > target){
r = mid - 1;
}
else
{
l = mid;
}
}
return
l;
}
};
|