2600. K Items With the Maximum Sum
Description
There is a bag that consists of items, each item has a number 1
, 0
, or -1
written on it.
You are given four non-negative integers numOnes
, numZeros
, numNegOnes
, and k
.
The bag initially contains:
numOnes
items with1
s written on them.numZeroes
items with0
s written on them.numNegOnes
items with-1
s written on them.
We want to pick exactly k
items among the available items. Return the maximum possible sum of numbers written on the items.
Example 1:
Input: numOnes = 3, numZeros = 2, numNegOnes = 0, k = 2 Output: 2 Explanation: We have a bag of items with numbers written on them {1, 1, 1, 0, 0}. We take 2 items with 1 written on them and get a sum in a total of 2. It can be proven that 2 is the maximum possible sum.
Example 2:
Input: numOnes = 3, numZeros = 2, numNegOnes = 0, k = 4 Output: 3 Explanation: We have a bag of items with numbers written on them {1, 1, 1, 0, 0}. We take 3 items with 1 written on them, and 1 item with 0 written on it, and get a sum in a total of 3. It can be proven that 3 is the maximum possible sum.
Constraints:
0 <= numOnes, numZeros, numNegOnes <= 50
0 <= k <= numOnes + numZeros + numNegOnes
Solutions
Solution 1: Greedy
According to the problem description, we should take as many items marked as \(1\) as possible, then take items marked as \(0\), and finally take items marked as \(-1\).
Thus:
- If the number of items marked as \(1\) in the bag is greater than or equal to \(k\), we take \(k\) items, and the sum of the numbers is \(k\).
- If the number of items marked as \(1\) is less than \(k\), we take \(\textit{numOnes}\) items, resulting in a sum of \(\textit{numOnes}\). If the number of items marked as \(0\) is greater than or equal to \(k - \textit{numOnes}\), we take \(k - \textit{numOnes}\) more items, keeping the sum at \(\textit{numOnes}\).
- Otherwise, we take \(k - \textit{numOnes} - \textit{numZeros}\) items from those marked as \(-1\), resulting in a sum of \(\textit{numOnes} - (k - \textit{numOnes} - \textit{numZeros})\).
The time complexity is \(O(1)\), and the space complexity is \(O(1)\).
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