# Posts tagged as “binary search”

There is a hotel with n rooms. The rooms are represented by a 2D integer array rooms where rooms[i] = [roomIdi, sizei] denotes that there is a room with room number roomIdi and size equal to sizei. Each roomIdi is guaranteed to be unique.

You are also given k queries in a 2D array queries where queries[j] = [preferredj, minSizej]. The answer to the jth query is the room number id of a room such that:

• The room has a size of at least minSizej, and
• abs(id - preferredj) is minimized, where abs(x) is the absolute value of x.

If there is a tie in the absolute difference, then use the room with the smallest such id. If there is no such room, the answer is -1.

Return an array answer of length k where answer[j] contains the answer to the jth query.

Example 1:

Input: rooms = [[2,2],[1,2],[3,2]], queries = [[3,1],[3,3],[5,2]]
Output: [3,-1,3]
Explanation: The answers to the queries are as follows:
Query = [3,1]: Room number 3 is the closest as abs(3 - 3) = 0, and its size of 2 is at least 1. The answer is 3.
Query = [3,3]: There are no rooms with a size of at least 3, so the answer is -1.
Query = [5,2]: Room number 3 is the closest as abs(3 - 5) = 2, and its size of 2 is at least 2. The answer is 3.

Example 2:

Input: rooms = [[1,4],[2,3],[3,5],[4,1],[5,2]], queries = [[2,3],[2,4],[2,5]]
Output: [2,1,3]
Explanation: The answers to the queries are as follows:
Query = [2,3]: Room number 2 is the closest as abs(2 - 2) = 0, and its size of 3 is at least 3. The answer is 2.
Query = [2,4]: Room numbers 1 and 3 both have sizes of at least 4. The answer is 1 since it is smaller.
Query = [2,5]: Room number 3 is the only room with a size of at least 5. The answer is 3.

Constraints:

• n == rooms.length
• 1 <= n <= 105
• k == queries.length
• 1 <= k <= 104
• 1 <= roomIdi, preferredj <= 107
• 1 <= sizei, minSizej <= 107

## Solution: Off Processing: Sort + Binary Search

Time complexity: O(nlogn + mlogm)
Space complexity: O(n + m)

Sort queries and rooms by size in descending order, only add valid rooms (size >= min_size) to the treeset for binary search.

## Solution 2: Fenwick Tree

Time complexity: O(nlogS + mlogSlogn)
Space complexity: O(nlogS)

## C++

You are given two positive integer arrays nums1 and nums2, both of length n.

The absolute sum difference of arrays nums1 and nums2 is defined as the sum of |nums1[i] - nums2[i]| for each 0 <= i < n (0-indexed).

You can replace at most one element of nums1 with any other element in nums1 to minimize the absolute sum difference.

Return the minimum absolute sum difference after replacing at most oneelement in the array nums1. Since the answer may be large, return it modulo 109 + 7.

|x| is defined as:

• x if x >= 0, or
• -x if x < 0.

Example 1:

Input: nums1 = [1,7,5], nums2 = [2,3,5]
Output: 3
Explanation: There are two possible optimal solutions:
- Replace the second element with the first: [1,7,5] => [1,1,5], or
- Replace the second element with the third: [1,7,5] => [1,5,5].
Both will yield an absolute sum difference of |1-2| + (|1-3| or |5-3|) + |5-5| = 3.


Example 2:

Input: nums1 = [2,4,6,8,10], nums2 = [2,4,6,8,10]
Output: 0
Explanation: nums1 is equal to nums2 so no replacement is needed. This will result in an
absolute sum difference of 0.


Example 3:

Input: nums1 = [1,10,4,4,2,7], nums2 = [9,3,5,1,7,4]
Output: 20
Explanation: Replace the first element with the second: [1,10,4,4,2,7] => [10,10,4,4,2,7].
This yields an absolute sum difference of |10-9| + |10-3| + |4-5| + |4-1| + |2-7| + |7-4| = 20


Constraints:

• n == nums1.length
• n == nums2.length
• 1 <= n <= 105
• 1 <= nums1[i], nums2[i] <= 105

## Solution: Binary Search

Greedy won’t work, e.g. finding the max diff pair and replace it. Counter example:
nums1 = [7, 5], nums2 = [1, -2]
pair1 = abs(7 – 1) = 6
pair2 = abs(5 – (-2)) = 7
If we replace 5 with 7, we got pair2′ = abs(7 – (-2)) = 9 > 7.

Every pair of numbers can be the candidate, we just need to find the closest number for each nums2[i].

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

## C++

You are given three positive integers nindex and maxSum. You want to construct an array nums (0-indexed) that satisfies the following conditions:

• nums.length == n
• nums[i] is a positive integer where 0 <= i < n.
• abs(nums[i] - nums[i+1]) <= 1 where 0 <= i < n-1.
• The sum of all the elements of nums does not exceed maxSum.
• nums[index] is maximized.

Return nums[index] of the constructed array.

Note that abs(x) equals x if x >= 0, and -x otherwise.

Example 1:

Input: n = 4, index = 2,  maxSum = 6
Output: 2
Explanation: The arrays [1,1,2,1] and [1,2,2,1] satisfy all the conditions. There are no other valid arrays with a larger value at the given index.


Example 2:

Input: n = 6, index = 1,  maxSum = 10
Output: 3


Constraints:

• 1 <= n <= maxSum <= 109
• 0 <= index < n

## Solution: Binary Search

To maximize nums[index], we can construct an array like this:
[1, 1, 1, …, 1, 2, 3, …, k – 1, k, k – 1, …,3, 2, 1, …., 1, 1, 1]

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

## C++

You are given an integer array nums where the ith bag contains nums[i] balls. You are also given an integer maxOperations.

You can perform the following operation at most maxOperations times:

• Take any bag of balls and divide it into two new bags with a positive number of balls.
• For example, a bag of 5 balls can become two new bags of 1 and 4 balls, or two new bags of 2 and 3 balls.

Your penalty is the maximum number of balls in a bag. You want to minimize your penalty after the operations.

Return the minimum possible penalty after performing the operations.

Example 1:

Input: nums = [9], maxOperations = 2
Output: 3
Explanation:
- Divide the bag with 9 balls into two bags of sizes 6 and 3. [9] -> [6,3].
- Divide the bag with 6 balls into two bags of sizes 3 and 3. [6,3] -> [3,3,3].
The bag with the most number of balls has 3 balls, so your penalty is 3 and you should return 3.


Example 2:

Input: nums = [2,4,8,2], maxOperations = 4
Output: 2
Explanation:
- Divide the bag with 8 balls into two bags of sizes 4 and 4. [2,4,8,2] -> [2,4,4,4,2].
- Divide the bag with 4 balls into two bags of sizes 2 and 2. [2,4,4,4,2] -> [2,2,2,4,4,2].
- Divide the bag with 4 balls into two bags of sizes 2 and 2. [2,2,2,4,4,2] -> [2,2,2,2,2,4,2].
- Divide the bag with 4 balls into two bags of sizes 2 and 2. [2,2,2,2,2,4,2] -> [2,2,2,2,2,2,2,2].
The bag with the most number of balls has 2 balls, so your penalty is 2 an you should return 2.


Example 3:

Input: nums = [7,17], maxOperations = 2
Output: 7


Constraints:

• 1 <= nums.length <= 105
• 1 <= maxOperations, nums[i] <= 109

## Solution: Binary Search

Find the smallest penalty that requires less or equal ops than max_ops.

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

## C++

You are given an integer array nums and an integer goal.

You want to choose a subsequence of nums such that the sum of its elements is the closest possible to goal. That is, if the sum of the subsequence’s elements is sum, then you want to minimize the absolute difference abs(sum - goal).

Return the minimum possible value of abs(sum - goal).

Note that a subsequence of an array is an array formed by removing some elements (possibly all or none) of the original array.

Example 1:

Input: nums = [5,-7,3,5], goal = 6
Output: 0
Explanation: Choose the whole array as a subsequence, with a sum of 6.
This is equal to the goal, so the absolute difference is 0.


Example 2:

Input: nums = [7,-9,15,-2], goal = -5
Output: 1
Explanation: Choose the subsequence [7,-9,-2], with a sum of -4.
The absolute difference is abs(-4 - (-5)) = abs(1) = 1, which is the minimum.


Example 3:

Input: nums = [1,2,3], goal = -7
Output: 7


Constraints:

• 1 <= nums.length <= 40
• -107 <= nums[i] <= 107
• -109 <= goal <= 109

## Solution: Binary Search

Since n is too large to generate sums for all subsets O(2^n), we have to split the array into half, generate two sum sets. O(2^(n/2)).

Then the problem can be reduced to find the closet sum by picking one number (sum) each from two different arrays which can be solved in O(mlogm), where m = 2^(n/2).

So final time complexity is O(n * 2^(n/2))
Space complexity: O(2^(n/2))