How do you solve Solving Questions With Brainpower on LeetCode?

Solving Questions With Brainpower (LeetCode #2140) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Dynamic programming can significantly reduce redundant calculations by storing intermediate results.

What is the brute force solution for Solving Questions With Brainpower?

The brute force approach for Solving Questions With Brainpower is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute-force approach involves exploring all possible combinations of solving or skipping each question. We recursively decide for each question whether to solve it or skip it, calculating the total points earned for each scenario. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Solving Questions With Brainpower?

Solving Questions With Brainpower uses the following concepts: Array, Dynamic Programming. The recommended patterns to study are: Dynamic Programming, Recursion.

What are the interview tips for Solving Questions With Brainpower?

Clearly explain your thought process when deciding whether to solve or skip each question. Discuss the trade-offs of each approach and why you are moving towards a more optimal solution. Practice writing out the DP transitions on paper before coding to ensure clarity.

What are common mistakes when solving Solving Questions With Brainpower?

Not considering the base case properly in recursion, leading to stack overflow or incorrect results. Forgetting to handle the bounds when accessing the DP array, which can lead to index out of range errors.

#2140

Solving Questions With Brainpower

Medium

ArrayDynamic ProgrammingDynamic ProgrammingRecursion

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n)Space O(n)

Using dynamic programming, we can store the maximum points that can be earned starting from each question. This avoids redundant calculations and allows us to build the solution iteratively.

⚙️

Algorithm

3 steps

1Step 1: Create a DP array where dp[i] represents the maximum points from question i to the end.
2Step 2: Iterate backwards through the questions, calculating the maximum points for each question based on whether we solve or skip it.
3Step 3: For each question, update the DP array with the maximum points achievable.

solution.py14 lines

1# Full working Python code
2
3def maxPoints(questions):
4    n = len(questions)
5    dp = [0] * (n + 1)
6    for i in range(n - 1, -1, -1):
7        skip = dp[i + 1]
8        solve = questions[i][0] + (dp[i + questions[i][1] + 1] if i + questions[i][1] + 1 <= n else 0)
9        dp[i] = max(skip, solve)
10    return dp[0]
11
12# Example usage
13questions = [[3, 2], [4, 3], [4, 4], [2, 5]]
14print(maxPoints(questions))

ℹ

Complexity note: The time complexity is O(n) because we only iterate through the questions once, and the space complexity is O(n) due to the DP array used to store results.

1Dynamic programming can significantly reduce redundant calculations by storing intermediate results.
2The decision to solve or skip a question can be modeled as a recursive problem with overlapping subproblems.

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