How do you solve Maximize Sum Of Array After K Negations on LeetCode?

Maximize Sum Of Array After K Negations (LeetCode #1005) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(1) space complexity. Key insight: Negating the smallest elements first maximizes the sum.

What is the time complexity of Maximize Sum Of Array After K Negations?

The optimal solution for Maximize Sum Of Array After K Negations runs in O(n log n) time and uses O(1) space. The complexity is dominated by the sorting step, which is O(n log n). The subsequent operations are linear.

What is the brute force solution for Maximize Sum Of Array After K Negations?

The brute force approach for Maximize Sum Of Array After K Negations is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves trying every possible combination of negating elements in the array. By iterating through all possible indices and applying negations, we can find the maximum sum after k operations. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximize Sum Of Array After K Negations?

Maximize Sum Of Array After K Negations uses the following concepts: Array, Greedy, Sorting. The recommended patterns to study are: Greedy, Sorting.

What are the interview tips for Maximize Sum Of Array After K Negations?

Always consider edge cases, such as all elements being positive or negative. Explain your thought process clearly while coding. Practice similar problems to get comfortable with greedy algorithms.

What are common mistakes when solving Maximize Sum Of Array After K Negations?

Not sorting the array before negating elements. Failing to handle the case where k is greater than the number of negative elements.

#1005

Maximize Sum Of Array After K Negations

Easy

ArrayGreedySortingGreedySorting

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n log n)
Space	O(1)	O(1)

💡

Intuition

Time O(n log n)Space O(1)

The optimal approach focuses on negating the smallest elements first to maximize the overall sum. By sorting the array, we can efficiently determine which elements to negate.

⚙️

Algorithm

4 steps

1Step 1: Sort the array in ascending order.
2Step 2: Negate the smallest k elements (or fewer if there are not enough negative elements).
3Step 3: If k is still odd after negating, negate the smallest absolute value element.
4Step 4: Calculate the sum of the modified array.

solution.py10 lines

1# Full working Python code
2
3def maxSumAfterKNegations(nums, k):
4    nums.sort()
5    for i in range(min(k, len(nums))):
6        nums[i] = -nums[i]
7    if (k - len(nums)) % 2 == 1:
8        nums.sort()
9        nums[0] = -nums[0]
10    return sum(nums)

ℹ

Complexity note: The complexity is dominated by the sorting step, which is O(n log n). The subsequent operations are linear.

1Negating the smallest elements first maximizes the sum.
2If k is odd after negating, we need to negate the smallest absolute value element.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.