Posts published in “Binary Search”

花花酱 LeetCode 1552. Magnetic Force Between Two Balls

By zxi on August 15, 2020

In universe Earth C-137, Rick discovered a special form of magnetic force between two balls if they are put in his new invented basket. Rick has n empty baskets, the i^th basket is at position[i], Morty has m balls and needs to distribute the balls into the baskets such that the minimum magnetic force between any two balls is maximum.

Rick stated that magnetic force between two different balls at positions x and y is |x - y|.

Given the integer array position and the integer m. Return the required force.

Example 1:

Input: position = [1,2,3,4,7], m = 3
Output: 3
Explanation: Distributing the 3 balls into baskets 1, 4 and 7 will make the magnetic force between ball pairs [3, 3, 6]. The minimum magnetic force is 3. We cannot achieve a larger minimum magnetic force than 3.

Example 2:

Input: position = [5,4,3,2,1,1000000000], m = 2
Output: 999999999
Explanation: We can use baskets 1 and 1000000000.

Constraints:

n == position.length
2 <= n <= 10^5
1 <= position[i] <= 10^9
All integers in position are distinct.
2 <= m <= position.length

Solution: Binary Search

Find the max distance that we can put m balls.

Time complexity: O(n*log(distance))
Space complexity: O(1)

C++

class Solution {

public:

int maxDistance(vector<int>& position, int m) {

const int n = position.size();

sort(begin(position), end(position));

auto count = [&](int d) -> int {

int last = position[0];

int ans = 1;

for (int x : position) {

if (x - last >= d) {

last = x;

++ans;

}

return ans;

};

int l = 0;

int t = (position.back() - position.front()) + 1;

int r = t;

while (l < r) {

int mid = l + (r - l) / 2;

if (count(t - mid) >= m) // find min of l => max of t - l

r = mid;

else

l = mid + 1;

}

return t - l;

}

};

Java

class Solution {

public int maxDistance(int[] position, int m) {

Arrays.sort(position);

final int n = position.length;

int l = 0;

int r = position[n - 1] - position[0] + 1;

int t = r;

while (l < r) {

int mid = l + (r - l) / 2;

if (getCount(position, t - mid) >= m)

r = mid;

else

l = mid + 1;

}

return t - l;

}

private int getCount(int[] position, int d) {

int count = 1;

int last = position[0];

for (int x : position) {

if (x - last >= d) {

++count;

last = x;

}

return count;

}

Python3

class Solution:

def maxDistance(self, position: List[int], m: int) -> int:

position.sort()

def getCount(d: int) -> int:

last, count = position[0], 1

for x in position:

if x - last >= d:

last = x

count += 1

return count

l, r = 0, position[-1] - position[0] + 1

t = r

while l < r:

mid = l + (r - l) // 2

if getCount(t - mid) >= m:

r = mid

else:

l = mid + 1

return t - l

花花酱 LeetCode 1539. Kth Missing Positive Number

By zxi on August 9, 2020

Given an array arr of positive integers sorted in a strictly increasing order, and an integer k.

Find the k^th positive integer that is missing from this array.

Example 1:

Input: arr = [2,3,4,7,11], k = 5
Output: 9
Explanation: The missing positive integers are [1,5,6,8,9,10,12,13,...]. The 5^th missing positive integer is 9.

Example 2:

Input: arr = [1,2,3,4], k = 2
Output: 6
Explanation: The missing positive integers are [5,6,7,...]. The 2^nd missing positive integer is 6.

Constraints:

1 <= arr.length <= 1000
1 <= arr[i] <= 1000
1 <= k <= 1000
arr[i] < arr[j] for 1 <= i < j <= arr.length

Solution 1: HashTable

Store all the elements into a hashtable, and check from 1 to max(arr).

Time complexity: O(max(arr)) ~ O(1000)
Space complexity: O(n)

C++

// Author: Huahua

class Solution {

public:

int findKthPositive(vector<int>& arr, int k) {

unordered_set<int> s(begin(arr), end(arr));

for (int i = 1; i <= arr.back(); ++i) {

if (!s.count(i)) --k;

if (k == 0) return i;

}

return arr.back() + k;

}

};

Solution 2: Binary Search

We can find the smallest index l using binary search, s.t.
arr[l] – l + 1 >= k
which means we missed at least k numbers at index l.
And the answer will be l + k.

Time complexity: O(logn)
Space complexity: O(1)

C++

// Author: Huahua

class Solution {

public:

int findKthPositive(vector<int>& arr, int k) {

int l = 0;

int r = arr.size();

while (l < r) {

int m = l + (r - l) / 2;

if (arr[m] - (m + 1) >= k)

r = m;

else

l = m + 1;

}

return l + k;

}

};

Java

class Solution {

public int findKthPositive(int[] arr, int k) {

int l = 0;

int r = arr.length;

while (l < r) {

int m = l + (r - l) / 2;

if (arr[m] - (m + 1) >= k)

r = m;

else

l = m + 1;

}

return l + k;

}

Python3

class Solution:

def findKthPositive(self, arr: List[int], k: int) -> int:

l, r = 0, len(arr)

while l < r:

m = l + (r - l) // 2

if arr[m] - (m + 1) >= k:

r = m

else:

l = m + 1

return l + k

花花酱 LeetCode 1488. Avoid Flood in The City

By zxi on June 20, 2020

Your country has an infinite number of lakes. Initially, all the lakes are empty, but when it rains over the nth lake, the nth lake becomes full of water. If it rains over a lake which is full of water, there will be a flood. Your goal is to avoid the flood in any lake.

Given an integer array rains where:

rains[i] > 0 means there will be rains over the rains[i] lake.
rains[i] == 0 means there are no rains this day and you can choose one lake this day and dry it.

Return an array ans where:

ans.length == rains.length
ans[i] == -1 if rains[i] > 0.
ans[i] is the lake you choose to dry in the ith day if rains[i] == 0.

If there are multiple valid answers return any of them. If it is impossible to avoid flood return an empty array.

Notice that if you chose to dry a full lake, it becomes empty, but if you chose to dry an empty lake, nothing changes. (see example 4)

Example 1:

Input: rains = [1,2,3,4]
Output: [-1,-1,-1,-1]
Explanation: After the first day full lakes are [1]
After the second day full lakes are [1,2]
After the third day full lakes are [1,2,3]
After the fourth day full lakes are [1,2,3,4]
There's no day to dry any lake and there is no flood in any lake.

Example 2:

Input: rains = [1,2,0,0,2,1]
Output: [-1,-1,2,1,-1,-1]
Explanation: After the first day full lakes are [1]
After the second day full lakes are [1,2]
After the third day, we dry lake 2. Full lakes are [1]
After the fourth day, we dry lake 1. There is no full lakes.
After the fifth day, full lakes are [2].
After the sixth day, full lakes are [1,2].
It is easy that this scenario is flood-free. [-1,-1,1,2,-1,-1] is another acceptable scenario.

Example 3:

Input: rains = [1,2,0,1,2]
Output: []
Explanation: After the second day, full lakes are  [1,2]. We have to dry one lake in the third day.
After that, it will rain over lakes [1,2]. It's easy to prove that no matter which lake you choose to dry in the 3rd day, the other one will flood.

Example 4:

Input: rains = [69,0,0,0,69]
Output: [-1,69,1,1,-1]
Explanation: Any solution on one of the forms [-1,69,x,y,-1], [-1,x,69,y,-1] or [-1,x,y,69,-1] is acceptable where 1 <= x,y <= 10^9

Example 5:

Input: rains = [10,20,20]
Output: []
Explanation: It will rain over lake 20 two consecutive days. There is no chance to dry any lake.

Constraints:

1 <= rains.length <= 10^5
0 <= rains[i] <= 10^9

Solution: Binary Search

Store the days we can dry a lake in a treeset.
Store the last day when a lake becomes full in a hashtable.
Whenever we encounter a full lake, try to find the first available day that we can dry it. If no such day, return no answer.

Time complexity: O(nlogn)
Space complexity: O(n)

C++

// Author: Huahua

class Solution {

public:

vector<int> avoidFlood(vector<int>& rains) {

const int n = rains.size();

vector<int> ans(n, -1);

unordered_map<int, int> full; // lake -> day

set<int> dry; // days we can dry lakes.

for (int i = 0; i < n; ++i) {

const int lake = rains[i];

if (lake > 0) {

if (full.count(lake)) {

// Find the first day we can dry it.

auto it = dry.upper_bound(full[lake]);

if (it == end(dry)) return {};

ans[*it] = lake;

dry.erase(it);

}

full[lake] = i;

} else {

dry.insert(i);

ans[i] = 1;

}

return ans;

}

};

花花酱 LeetCode 1482. Minimum Number of Days to Make m Bouquets

By zxi on June 14, 2020

Given an integer array bloomDay, an integer m and an integer k.

We need to make m bouquets. To make a bouquet, you need to use k adjacent flowers from the garden.

The garden consists of n flowers, the ith flower will bloom in the bloomDay[i] and then can be used in exactly one bouquet.

Return the minimum number of days you need to wait to be able to make m bouquets from the garden. If it is impossible to make m bouquets return -1.

Example 1:

Input: bloomDay = [1,10,3,10,2], m = 3, k = 1
Output: 3
Explanation: Let's see what happened in the first three days. x means flower bloomed and _ means flower didn't bloom in the garden.
We need 3 bouquets each should contain 1 flower.
After day 1: [x, _, _, _, _]   // we can only make one bouquet.
After day 2: [x, _, _, _, x]   // we can only make two bouquets.
After day 3: [x, _, x, _, x]   // we can make 3 bouquets. The answer is 3.

Example 2:

Input: bloomDay = [1,10,3,10,2], m = 3, k = 2
Output: -1
Explanation: We need 3 bouquets each has 2 flowers, that means we need 6 flowers. We only have 5 flowers so it is impossible to get the needed bouquets and we return -1.

Example 3:

Input: bloomDay = [7,7,7,7,12,7,7], m = 2, k = 3
Output: 12
Explanation: We need 2 bouquets each should have 3 flowers.
Here's the garden after the 7 and 12 days:
After day 7: [x, x, x, x, _, x, x]
We can make one bouquet of the first three flowers that bloomed. We cannot make another bouquet from the last three flowers that bloomed because they are not adjacent.
After day 12: [x, x, x, x, x, x, x]
It is obvious that we can make two bouquets in different ways.

Example 4:

Input: bloomDay = [1000000000,1000000000], m = 1, k = 1
Output: 1000000000
Explanation: You need to wait 1000000000 days to have a flower ready for a bouquet.

Example 5:

Input: bloomDay = [1,10,2,9,3,8,4,7,5,6], m = 4, k = 2
Output: 9

Constraints:

bloomDay.length == n
1 <= n <= 10^5
1 <= bloomDay[i] <= 10^9
1 <= m <= 10^6
1 <= k <= n

Solution: Binary Search

Find the smallest day D that we can make at least m bouquets using binary search.

at a given day, we can check how many bouquets we can make in O(n)

Time complexity: O(nlog(max(days))
Space complexity: O(1)

C++

// Author: Huahua

class Solution {

public:

int minDays(vector<int>& bloomDay, int m, int k) {

const auto [lo, hi] = minmax_element(begin(bloomDay), end(bloomDay));

const int kInf = *hi + 1;

int l = *lo;

int r = kInf;

// Return the number of bouquets we can get at day D.

auto getBouquets = [&](int D) {

int ans = 0;

int cur = 0;

for (int d : bloomDay) {

if (d > D) {

cur = 0;

} else if (++cur == k) {

++ans;

cur = 0;

}

return ans;

};

while (l < r) {

int mid = l + (r - l) / 2;

// Find smallest day that bouquets >= m.

if (getBouquets(mid) >= m)

r = mid;

else

l = mid + 1;

}

return l >= kInf ? -1 : l;

}

};

花花酱 LeetCode 1348. Tweet Counts Per Frequency

By zxi on February 29, 2020

Implement the class TweetCounts that supports two methods:

1. recordTweet(string tweetName, int time)

Stores the tweetName at the recorded time (in seconds).

2. getTweetCountsPerFrequency(string freq, string tweetName, int startTime, int endTime)

Returns the total number of occurrences for the given tweetName per minute, hour, or day (depending on freq) starting from the startTime (in seconds) and ending at the endTime (in seconds).
freq is always minute, hour or day, representing the time interval to get the total number of occurrences for the given tweetName.
The first time interval always starts from the startTime, so the time intervals are [startTime, startTime + delta*1>, [startTime + delta*1, startTime + delta*2>, [startTime + delta*2, startTime + delta*3>, ... , [startTime + delta*i, min(startTime + delta*(i+1), endTime + 1)> for some non-negative number i and delta (which depends on freq).

Example:

Input
["TweetCounts","recordTweet","recordTweet","recordTweet","getTweetCountsPerFrequency","getTweetCountsPerFrequency","recordTweet","getTweetCountsPerFrequency"]
[[],["tweet3",0],["tweet3",60],["tweet3",10],["minute","tweet3",0,59],["minute","tweet3",0,60],["tweet3",120],["hour","tweet3",0,210]]

Output
[null,null,null,null,[2]
[2,1],null,[4]]

Explanation
TweetCounts tweetCounts = new TweetCounts();
tweetCounts.recordTweet("tweet3", 0);
tweetCounts.recordTweet("tweet3", 60);
tweetCounts.recordTweet("tweet3", 10);                             // All tweets correspond to "tweet3" with recorded times at 0, 10 and 60.
tweetCounts.getTweetCountsPerFrequency("minute", "tweet3", 0, 59); // return [2]. The frequency is per minute (60 seconds), so there is one interval of time: 1) [0, 60> - > 2 tweets.
tweetCounts.getTweetCountsPerFrequency("minute", "tweet3", 0, 60); // return [2, 1]. The frequency is per minute (60 seconds), so there are two intervals of time: 1) [0, 60> - > 2 tweets, and 2) [60,61> - > 1 tweet.
tweetCounts.recordTweet("tweet3", 120);                            // All tweets correspond to "tweet3" with recorded times at 0, 10, 60 and 120.
tweetCounts.getTweetCountsPerFrequency("hour", "tweet3", 0, 210);  // return [4]. The frequency is per hour (3600 seconds), so there is one interval of time: 1) [0, 211> - > 4 tweets.

Constraints:

There will be at most 10000 operations considering both recordTweet and getTweetCountsPerFrequency.
0 <= time, startTime, endTime <= 10^9

Solution: Hashtable + binary search

Time complexity:
Record: O(logn)
getCount: O(logn + |entries|)

Space complexity: O(n)

C++

// Author: Huahua

class TweetCounts {

public:

TweetCounts() {

m_.clear();

}

void recordTweet(string tweetName, int time) {

m_[tweetName].insert(time);

}

vector<int> getTweetCountsPerFrequency(string freq, string tweetName, int startTime, int endTime) {

auto tit = m_.find(tweetName);

if (tit == m_.end()) return {};

const set<int>& s = tit->second;

int delta = 1;

if (freq == "minute") delta = 60;

else if (freq == "hour") delta = 60 * 60;

else if (freq == "day") delta = 60 * 60 * 24;

int slots = (endTime - startTime + delta - 1) / delta;

vector<int> ans(slots);

for (int i = startTime; i <= endTime; i += delta) {

auto it1 = s.lower_bound(i);

auto it2 = s.lower_bound(min(i + delta, endTime + 1));

ans.push_back(distance(it1, it2)); // O(|entries|)

}

return ans;

}

private:

unordered_map<string, set<int>> m_;

};