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How to Solve Maximum Points in an Archery Competition Problem
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Start Free for WindowsMedium#2212
LeetCode ProblemMaximum Points in an Archery Competition
Alice and Bob are opponents in an archery competition. The competition has set the following rules: You are given the integer numArrows and an integer array aliceArrows of size 12, which represents the number of arrows Alice shot on each scoring section from 0 to 11. Now, Bob wants to maximize the total number of points he can obtain. Return the array bobArrows which represents the number of arrows Bob shot on each scoring section from 0 to 11. The sum of the values in bobArrows should equal numArrows. If there are multiple ways for Bob to earn the maximum total points, return any one of them.
ArrayBacktrackingBit ManipulationEnumeration
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Problem Breakdown
Understanding the Maximum Points in an Archery Competition Problem
Let's break down this LeetCode problem and understand what makes it challenging in interview settings.
Problem Statement
Alice and Bob are opponents in an archery competition. The competition has set the following rules: You are given the integer numArrows and an integer array aliceArrows of size 12, which represents the number of arrows Alice shot on each scoring section from 0 to 11. Now, Bob wants to maximize the total number of points he can obtain. Return the array bobArrows which represents the number of arrows Bob shot on each scoring section from 0 to 11. The sum of the values in bobArrows should equal numArrows. If there are multiple ways for Bob to earn the maximum total points, return any one of them.
MediumProblem #2212
LeetCodeMaximum Points in an Archery Competition
Related Topics
ArrayBacktrackingBit ManipulationEnumeration
How Phantom Code Helps
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Examples
# Example 1
Input
numArrows = 9, aliceArrows = [1,1,0,1,0,0,2,1,0,1,2,0]
Output
[0,0,0,0,1,1,0,0,1,2,3,1]
# Example 2
Input
numArrows = 3, aliceArrows = [0,0,1,0,0,0,0,0,0,0,0,2]
Output
[0,0,0,0,0,0,0,0,1,1,1,0]
Constraints
1 <= numArrows <= 105
aliceArrows.length == bobArrows.length == 12
0 <= aliceArrows[i], bobArrows[i] <= numArrows
sum(aliceArrows[i]) == numArrows
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Solve Maximum Points in an Archery Competition — Alice and Bob are opponents in an archery competition. The competition has set t...
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def solve(input):
# Optimal O(n) solution
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Time: O(n) | Space: O(n)
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Frequently Asked Questions
Common questions about solving Maximum Points in an Archery Competition and using Phantom Code during coding interviews.
How does Phantom Code help me solve coding problems like Maximum Points in an Archery Competition during interviews?
Phantom Code generates complete solutions instantly with proper complexity analysis, letting you focus on explaining your approach and demonstrating problem-solving skills rather than getting stuck on implementation details during high-pressure situations.
What if the interviewer asks follow-up questions or modifications to problems like Maximum Points in an Archery Competition?
Phantom Code adapts in real-time. Screenshot the modified problem or use audio mode to capture the interviewer's follow-up, and get an updated solution within seconds — including edge cases and optimizations.
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Yes. Phantom Code provides step-by-step approach explanations with 3 progressive thoughts, so you can walk through your solution naturally. It also provides time and space complexity analysis to discuss trade-offs.
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Phantom Code supports 11 programming languages including Python, Java, C++, JavaScript, TypeScript, Go, Rust, Ruby, Swift, Kotlin, and C#. Set your preferred language and get idiomatic solutions.
What coding mistakes does Phantom Code help me avoid during Maximum Points in an Archery Competition interviews?
Phantom Code catches common mistakes like off-by-one errors, missing edge cases, incorrect base conditions, and suboptimal approaches. It provides clean, well-structured code that handles all edge cases.
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How does Phantom Code help when I'm struggling with Maximum Points in an Archery Competition style questions under pressure?
The AI provides structured guidance: first the approach (what algorithm/data structure to use and why), then the implementation with clean code, and finally complexity analysis. This helps you think clearly even under pressure.
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