{ "data": { "question": { "questionId": "2952", "questionFrontendId": "2809", "boundTopicId": null, "title": "Minimum Time to Make Array Sum At Most x", "titleSlug": "minimum-time-to-make-array-sum-at-most-x", "content": "

You are given two 0-indexed integer arrays nums1 and nums2 of equal length. Every second, for all indices 0 <= i < nums1.length, value of nums1[i] is incremented by nums2[i]. After this is done, you can do the following operation:

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You are also given an integer x.

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Return the minimum time in which you can make the sum of all elements of nums1 to be less than or equal to x, or -1 if this is not possible.

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Example 1:

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\nInput: nums1 = [1,2,3], nums2 = [1,2,3], x = 4\nOutput: 3\nExplanation: \nFor the 1st second, we apply the operation on i = 0. Therefore nums1 = [0,2+2,3+3] = [0,4,6]. \nFor the 2nd second, we apply the operation on i = 1. Therefore nums1 = [0+1,0,6+3] = [1,0,9]. \nFor the 3rd second, we apply the operation on i = 2. Therefore nums1 = [1+1,0+2,0] = [2,2,0]. \nNow sum of nums1 = 4. It can be shown that these operations are optimal, so we return 3.\n\n
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Example 2:

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\nInput: nums1 = [1,2,3], nums2 = [3,3,3], x = 4\nOutput: -1\nExplanation: It can be shown that the sum of nums1 will always be greater than x, no matter which operations are performed.\n
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Constraints:

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It can be proven that in the optimal solution, for each index i, we only need to set nums1[i] to 0 at most once. (If we have to set it twice, we can simply remove the earlier set and all the operations “shift left” by 1.)
", "
It can also be proven that if we select several indexes i1, i2, ..., ik and set nums1[i1], nums1[i2], ..., nums1[ik] to 0, it’s always optimal to set them in the order of nums2[i1] <= nums2[i2] <= ... <= nums2[ik] (the larger the increase is, the later we should set it to 0).
", "
Let’s sort all the values by nums2 (in non-decreasing order). Let dp[i][j] represent the maximum total value that can be reduced if we do j operations on the first i elements. Then we have dp[i][0] = 0 (for all i = 0, 1, ..., n) and dp[i][j] = max(dp[i - 1][j], dp[i - 1][j - 1] + nums2[i - 1] * j + nums1[i - 1]) (for 1 <= i <= n and 1 <= j <= i).
", "
The answer is the minimum value of t, such that 0 <= t <= n and sum(nums1) + sum(nums2) * t - dp[n][t] <= x, or -1 if it doesn’t exist.
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