How do you solve Maximize Points After Choosing K Tasks on LeetCode?

Maximize Points After Choosing K Tasks (LeetCode #3767) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(n) space complexity. Key insight: Maximize points by strategically choosing tasks.

What is the time complexity of Maximize Points After Choosing K Tasks?

The optimal solution for Maximize Points After Choosing K Tasks runs in O(n log n) time and uses O(n) space. Sorting the deltas takes O(n log n), and the space is used for the deltas array.

What is the brute force solution for Maximize Points After Choosing K Tasks?

The brute force approach for Maximize Points After Choosing K Tasks is Brute Force, with O(n²) time complexity and O(1) space complexity. Try every combination of tasks to see which yields the highest points. This is inefficient as it checks all possible selections. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Maximize Points After Choosing K Tasks?

Maximize Points After Choosing K Tasks uses the following concepts: Array, Greedy, Sorting, Heap (Priority Queue). The recommended patterns to study are: Greedy, Sorting, Heap.

What are the interview tips for Maximize Points After Choosing K Tasks?

Explain your thought process clearly. Discuss edge cases, like all tasks being from one technique. Practice similar problems to build confidence.

What are common mistakes when solving Maximize Points After Choosing K Tasks?

Not ensuring at least k tasks are from technique1. Miscalculating the delta values.

#3767

Maximize Points After Choosing K Tasks

Medium

ArrayGreedySortingHeap (Priority Queue)GreedySortingHeap

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

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Intuition

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

Start by completing all tasks with technique1, then selectively switch to technique2 for the tasks that yield the highest delta in points.

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Algorithm

3 steps

1Step 1: Calculate the initial points using all tasks from technique1.
2Step 2: Create a list of deltas (points gained by switching to technique2) and sort it in descending order.
3Step 3: Add the top (n-k) deltas to the initial points to maximize the total.

solution.py7 lines

1def maxPoints(technique1, technique2, k):
2    n = len(technique1)
3    total_points = sum(technique1)
4    deltas = [technique2[i] - technique1[i] for i in range(n)]
5    deltas.sort(reverse=True)
6    total_points += sum(deltas[i] for i in range(n - k))
7    return total_points

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Complexity note: Sorting the deltas takes O(n log n), and the space is used for the deltas array.

1Maximize points by strategically choosing tasks.
2Sorting helps identify the best tasks to switch techniques.

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