Zero Array Transformation IV
MEDIUMDescription
You are given an integer array nums of length n and a 2D array queries, where queries[i] = [li, ri, vali].
Each queries[i] represents the following action on nums:
- Select a subset of indices in the range
[li, ri]fromnums. - Decrement the value at each selected index by exactly
vali.
A Zero Array is an array with all its elements equal to 0.
Return the minimum possible non-negative value of k, such that after processing the first k queries in sequence, nums becomes a Zero Array. If no such k exists, return -1.
Example 1:
Input: nums = [2,0,2], queries = [[0,2,1],[0,2,1],[1,1,3]]
Output: 2
Explanation:
- For query 0 (l = 0, r = 2, val = 1):
- Decrement the values at indices
[0, 2]by 1. - The array will become
[1, 0, 1].
- Decrement the values at indices
- For query 1 (l = 0, r = 2, val = 1):
- Decrement the values at indices
[0, 2]by 1. - The array will become
[0, 0, 0], which is a Zero Array. Therefore, the minimum value ofkis 2.
- Decrement the values at indices
Example 2:
Input: nums = [4,3,2,1], queries = [[1,3,2],[0,2,1]]
Output: -1
Explanation:
It is impossible to make nums a Zero Array even after all the queries.
Example 3:
Input: nums = [1,2,3,2,1], queries = [[0,1,1],[1,2,1],[2,3,2],[3,4,1],[4,4,1]]
Output: 4
Explanation:
- For query 0 (l = 0, r = 1, val = 1):
- Decrement the values at indices
[0, 1]by1. - The array will become
[0, 1, 3, 2, 1].
- Decrement the values at indices
- For query 1 (l = 1, r = 2, val = 1):
- Decrement the values at indices
[1, 2]by 1. - The array will become
[0, 0, 2, 2, 1].
- Decrement the values at indices
- For query 2 (l = 2, r = 3, val = 2):
- Decrement the values at indices
[2, 3]by 2. - The array will become
[0, 0, 0, 0, 1].
- Decrement the values at indices
- For query 3 (l = 3, r = 4, val = 1):
- Decrement the value at index 4 by 1.
- The array will become
[0, 0, 0, 0, 0]. Therefore, the minimum value ofkis 4.
Example 4:
Input: nums = [1,2,3,2,6], queries = [[0,1,1],[0,2,1],[1,4,2],[4,4,4],[3,4,1],[4,4,5]]
Output: 4
Constraints:
1 <= nums.length <= 100 <= nums[i] <= 10001 <= queries.length <= 1000queries[i] = [li, ri, vali]0 <= li <= ri < nums.length1 <= vali <= 10
Approaches
Checkout 3 different approaches to solve Zero Array Transformation IV. Click on different approaches to view the approach and algorithm in detail.
Brute Force with Backtracking
This approach simulates the process for every possible number of queries k, from 1 up to the total number of queries. For each k, it checks if it's possible to make the array nums all zeros. This check is done by solving a subset sum problem for each element nums[j]. The subset sum problem itself is solved using a brute-force backtracking (or recursive) method, which explores all possibilities.
Algorithm
- For each possible number of queries
kfrom 1 toqueries.length:- Call a function
canMakeZero(k)to check ifnumscan be turned into a zero array. - If
canMakeZero(k)returnstrue, thenkis the minimum number of queries, so returnk.
- Call a function
- If the loop completes without finding a solution, return -1.
- The
canMakeZero(k)function checks each indexjofnums:- It gathers all
val_ifrom the firstkqueries that are applicable to indexj. - It uses a recursive backtracking function to check if
nums[j]can be formed by a sum of a subset of these values. - If any index
jfails this check,canMakeZero(k)returnsfalse. - If all indices pass, it returns
true.
- It gathers all
The core idea is to test each potential answer k sequentially. For a given k, we must determine if there's a way to apply the first k queries to make every nums[j] zero. This decomposes into n independent subset sum problems. For each nums[j], we need to find if it can be expressed as a sum of some of the val_i from the first k queries that cover index j.
The backtracking function canFormSum explores all subsets of the applicable query values to see if any sum up to the target nums[j]. It does this by making two recursive calls at each step: one where the current value is included in the sum, and one where it's excluded.
class Solution {
public int zeroArray(int[] nums, int[][] queries) {
for (int k = 1; k <= queries.length; k++) {
if (canMakeZero(nums, queries, k)) {
return k;
}
}
return -1;
}
private boolean canMakeZero(int[] nums, int[][] queries, int k) {
for (int j = 0; j < nums.length; j++) {
if (nums[j] == 0) continue;
java.util.List<Integer> options = new java.util.ArrayList<>();
for (int i = 0; i < k; i++) {
if (queries[i][0] <= j && j <= queries[i][1]) {
options.add(queries[i][2]);
}
}
if (!canFormSum(nums[j], options, 0)) {
return false;
}
}
return true;
}
private boolean canFormSum(int target, java.util.List<Integer> options, int index) {
if (target == 0) return true;
if (target < 0 || index == options.size()) return false;
// Exclude current option
if (canFormSum(target, options, index + 1)) {
return true;
}
// Include current option
if (canFormSum(target - options.get(index), options, index + 1)) {
return true;
}
return false;
}
}
Complexity Analysis
Pros and Cons
- Conceptually simple and easy to understand.
- Directly translates the problem statement into code.
- Extremely inefficient due to its exponential time complexity.
- Will result in a 'Time Limit Exceeded' error for all but the smallest inputs.
Video Solution
Watch the video walkthrough for Zero Array Transformation IV
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