2140. Solving Questions With Brainpower
Description
You are given a 0-indexed 2D integer array questions
where questions[i] = [pointsi, brainpoweri]
.
The array describes the questions of an exam, where you have to process the questions in order (i.e., starting from question 0
) and make a decision whether to solve or skip each question. Solving question i
will earn you pointsi
points but you will be unable to solve each of the next brainpoweri
questions. If you skip question i
, you get to make the decision on the next question.
- For example, given
questions = [[3, 2], [4, 3], [4, 4], [2, 5]]
:- If question
0
is solved, you will earn3
points but you will be unable to solve questions1
and2
. - If instead, question
0
is skipped and question1
is solved, you will earn4
points but you will be unable to solve questions2
and3
.
- If question
Return the maximum points you can earn for the exam.
Example 1:
Input: questions = [[3,2],[4,3],[4,4],[2,5]] Output: 5 Explanation: The maximum points can be earned by solving questions 0 and 3. - Solve question 0: Earn 3 points, will be unable to solve the next 2 questions - Unable to solve questions 1 and 2 - Solve question 3: Earn 2 points Total points earned: 3 + 2 = 5. There is no other way to earn 5 or more points.
Example 2:
Input: questions = [[1,1],[2,2],[3,3],[4,4],[5,5]] Output: 7 Explanation: The maximum points can be earned by solving questions 1 and 4. - Skip question 0 - Solve question 1: Earn 2 points, will be unable to solve the next 2 questions - Unable to solve questions 2 and 3 - Solve question 4: Earn 5 points Total points earned: 2 + 5 = 7. There is no other way to earn 7 or more points.
Constraints:
1 <= questions.length <= 105
questions[i].length == 2
1 <= pointsi, brainpoweri <= 105
Solutions
Solution 1: Memoization
We design a function \(\textit{dfs}(i)\), which represents the maximum score that can be obtained starting from the \(i\)-th question. The answer is \(\textit{dfs}(0)\).
The function \(\textit{dfs}(i)\) is calculated as follows:
- If \(i \geq n\), it means all questions have been solved, so return \(0\);
- Otherwise, let the score of the \(i\)-th question be \(p\), and the number of questions to skip be \(b\). Then, \(\textit{dfs}(i) = \max(p + \textit{dfs}(i + b + 1), \textit{dfs}(i + 1))\).
To avoid repeated calculations, we can use memoization by storing the values of \(\textit{dfs}(i)\) in an array \(f\).
The time complexity is \(O(n)\), and the space complexity is \(O(n)\), where \(n\) is the number of questions.
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Solution 2: Dynamic Programming
We define \(f[i]\) as the maximum score that can be obtained starting from the \(i\)-th problem. Therefore, the answer is \(f[0]\).
Considering \(f[i]\), let the score of the \(i\)-th problem be \(p\), and the number of problems to skip be \(b\). If we solve the \(i\)-th problem, then we need to solve the problem after skipping \(b\) problems, thus \(f[i] = p + f[i + b + 1]\). If we skip the \(i\)-th problem, then we start solving from the \((i + 1)\)-th problem, thus \(f[i] = f[i + 1]\). We take the maximum value of the two. The state transition equation is as follows:
We calculate the values of \(f\) from back to front, and finally return \(f[0]\).
The time complexity is \(O(n)\), and the space complexity is \(O(n)\). Here, \(n\) is the number of problems.
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