
Problem Statement
In this task, you are provided with an array of integers called nums
. The array indexes start at zero. You are allowed to perform an operation where any element of the array can be taken and moved to the end of the array as many times as needed.
Your goal is to determine the minimum number of these operations required so that when computing the prefix sum array of nums
, no entry in this prefix sum array is negative. The prefix sum array is defined such that its 'i-th' element is the sum of all elements from the 0-th to the i-th index in nums
.
It's guaranteed that a solution always exists where the prefix sums can all be made non-negative through a certain number of operations.
Examples
Example 1
Input:
nums = [2,3,-5,4]
Output:
0
Explanation:
we do not need to do any operations. The array is [2,3,-5,4]. The prefix sum array is [2, 5, 0, 4].
Example 2
Input:
nums = [3,-5,-2,6]
Output:
1
Explanation:
we can do one operation on index 1. The array after the operation is [3,-2,6,-5]. The prefix sum array is [3, 1, 7, 2].
Constraints
1 <= nums.length <= 105
-109 <= nums[i] <= 109
Approach and Intuition
First, recognize the problem as one of sorting or rearranging, so that while computing running totals from the start to each position, you never encounter a negative total.
The intuition here drives from the fact that:
- Negative numbers in the prefix can often be canceled out by moving some of the negative numbers towards the end, which leverages the positive figures to dominate.
- Examining each possible sequence and its resulting prefix without formal calculations can be overly complex. However, observing changes when specific negative numbers are deferred can give insights into operations.
For each example:
Example 1:
- Input:
nums = [2,3,-5,4]
- Output:
0
- Reasoning:
- The assessed prefix sums are
[2, 5, 0, 4]
. All entries are non-negative already, thus no moves are necessary.
- The assessed prefix sums are
- Input:
Example 2:
- Input:
nums = [3,-5,-2,6]
- Output:
1
- Reasoning:
- Initial prefix sums would have negatives (e.g., starting with
[3, -2, ...]
). By moving the-5
to the end, the sequence changes to[3,-2,6,-5]
resulting in non-negative prefix sums[3, 1, 7, 2]
.
- Initial prefix sums would have negatives (e.g., starting with
- Input:
As observed, transitioning through each element and evaluating the impact on prefix sums can guide where to strategically move elements in the array. Overall, we are aiming to optimize the sequence for prefix sums to stay non-negative with minimal rearrangements. Choices for operations will target the initial negative impacts in the prefix computation sequence.
Solutions
- C++
- Java
- Python
class Solution {
public:
int calculateMinOperations(vector<int>& elements) {
int totalOperations = 0;
long cumulativeSum = 0;
priority_queue<int, vector<int>, greater<int>> minHeap;
for (int element : elements) {
if (element < 0) {
minHeap.push(element);
}
cumulativeSum += element;
if (cumulativeSum < 0) {
cumulativeSum -= minHeap.top();
minHeap.pop();
totalOperations++;
}
}
return totalOperations;
}
};
Title: Make the Prefix Sum Non-negative
Programming Language: C++
Code Analysis:
The provided C++ function calculateMinOperations()
operates on an input vector elements
to compute the minimum number of operations required to make the prefix sum of array elements non-negative at all points. The method uses a few specific techniques as outlined:
Initialization:
totalOperations
starts at 0, representing the count of operations performed.cumulativeSum
, initialized at 0, stores the current sum of elements as the loop progresses.minHeap
is used to efficiently keep track of the negative elements in order to easily get the smallest (most negative) element when needed.
Iterative processing of the vector
elements
:- Traverse each element of the input vector:
- Push negative elements into
minHeap
. - Update
cumulativeSum
by adding the current element. - If
cumulativeSum
becomes negative, adjust it by subtracting the smallest element (i.e., the top of the priority queueminHeap
) and increase thetotalOperations
count by one. - Remove the used element from
minHeap
.
- Push negative elements into
- Traverse each element of the input vector:
Returning the result:
- After completing the iteration over all elements, the function returns the value of
totalOperations
, which reflects the minimum number of items that need to be removed to ensure no prefix sum is negative.
- After completing the iteration over all elements, the function returns the value of
This approach effectively ensures that the prefix sum remains non-negative, all while attempting to minimize the disturbance caused to the original array. The use of a minimum heap helps in efficiently accessing the smallest element, which is essential for correcting the negative sum promptly. By the end, the return value states how many elements had to be adjusted (removed from the sum in this case) to maintain non-negative prefix sums throughout the initial sequence.
class Solution {
public int minimizeNegativePrefix(int[] values) {
int countOps = 0;
long sumPrefix = 0;
PriorityQueue<Integer> minHeap = new PriorityQueue<>();
for (int value : values) {
if (value < 0) {
minHeap.offer(value);
}
sumPrefix += value;
if (sumPrefix < 0) {
sumPrefix -= minHeap.poll();
countOps++;
}
}
return countOps;
}
}
This Java solution addresses the problem of ensuring that the prefix sum of an array remains non-negative by performing a minimal number of operations. Each operation involves removing the smallest negative element from the prefix sum when it becomes negative.
Implementation Overview:
- The function
minimizeNegativePrefix
takes an integer array as input. - Two variables are initialized:
countOps
, which tracks the number of operations performed, andsumPrefix
, which keeps the running total of the prefix sum. - A
PriorityQueue
,minHeap
, is used to store negative numbers efficiently. Each negative element encountered is added to this priority queue. - As values are added to
sumPrefix
, the algorithm checks ifsumPrefix
becomes negative. - If
sumPrefix
is negative, the smallest value (which is negative) is removed fromminHeap
, andsumPrefix
is adjusted accordingly. ThecountOps
counter is incremented each time this adjustment is needed.
- The function
Detailed Steps:
- Traverse through each element in the input array.
- Add negative elements to
minHeap
. - Update
sumPrefix
with the current value. - If
sumPrefix
drops below zero, remove the smallest negative element fromminHeap
and adjustsumPrefix
. - Increment
countOps
each time an element is removed from the heap.
Expected Output:
- The function returns the count of operations performed (
countOps
), which is the minimum number needed to ensure the prefix sum never becomes negative.
- The function returns the count of operations performed (
This approach leverages a min-heap to efficiently remove the smallest negative numbers from the calculation, ensuring the prefix sum remains non-negative with minimal adjustments. This solution is optimal for scenarios where the sequence of numbers is unpredictable and could cause frequent drops below zero in cumulative sums.
class Solution:
def stabilizePrefixSum(self, values):
count_operations = 0
cumulative_sum = 0
min_heap = []
for value in values:
if value < 0:
heapq.heappush(min_heap, value)
cumulative_sum += value
if cumulative_sum < 0:
cumulative_sum -= heapq.heappop(min_heap)
count_operations += 1
return count_operations
The provided Python solution addresses the problem of making the prefix sum of an array non-negative. The function stabilizePrefixSum
in the Solution
class operates as follows:
- Initialize a variable
count_operations
to keep track of the number of operations needed. - Use
cumulative_sum
to track the running total of the array values. - A min-heap
min_heap
is employed to efficiently manage and retrieve minimum values when required.
The algorithm processes each element in the input list values
:
- If an element is negative, it is added to the
min_heap
. - The element is then added to
cumulative_sum
. - If
cumulative_sum
drops below zero, the smallest element is removed frommin_heap
and subtracted fromcumulative_sum
, andcount_operations
is incremented.
In summary, the code efficiently ensures the prefix sum of a list remains non-negative, using a min-heap to handle negative contributions and minimize their impact efficiently. The solution's effectiveness is achieved through careful management of the prefix sum and utilizing operations on the min-heap, leading to an optimal result in terms of operation count.
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