In this problem, Alice and Bob alternately pick numbers from a sorted array. Alice always picks the smallest remaining number, while Bob picks the next smallest. The game alternates and appends their picks to a result array. Sorting and simulation form the core of the solution.
Problem Statement
You are given an even-length integer array nums. Alice and Bob play a game where they take turns removing and appending elements from nums to a new array arr. Alice always removes the smallest available number, and Bob removes the next smallest. After removing, they append their picks to arr in the order they removed the numbers.
Your task is to return the array arr after all the rounds. Each round consists of Alice removing and appending the smallest element, followed by Bob removing and appending the next smallest element, until all elements are removed.
Examples
Example 1
Input: nums = [5,4,2,3]
Output: [3,2,5,4]
In round one, first Alice removes 2 and then Bob removes 3. Then in arr firstly Bob appends 3 and then Alice appends 2. So arr = [3,2]. At the begining of round two, nums = [5,4]. Now, first Alice removes 4 and then Bob removes 5. Then both append in arr which becomes [3,2,5,4].
Example 2
Input: nums = [2,5]
Output: [5,2]
In round one, first Alice removes 2 and then Bob removes 5. Then in arr firstly Bob appends and then Alice appends. So arr = [5,2].
Constraints
- 2 <= nums.length <= 100
- 1 <= nums[i] <= 100
- nums.length % 2 == 0
Solution Approach
Sorting the Array
Sort the array nums in increasing order before starting the game. This ensures that the smallest numbers are picked first by Alice, followed by the next smallest picked by Bob.
Simulating the Turns
After sorting, simulate the game by alternating between Alice and Bob. Alice picks the smallest remaining number, and Bob picks the next smallest. Append their selections to the result array arr in the order they picked.
Efficient Solution with Heap
To optimize, use a min-heap or priority queue to efficiently get the smallest elements during each turn. This reduces the need to sort repeatedly during the game simulation.
Complexity Analysis
| Metric | Value |
|---|---|
| Time | Depends on the final approach |
| Space | Depends on the final approach |
The time complexity depends on the sorting step, which is O(n log n) where n is the length of the array. Using a heap for the simulation can reduce the complexity for picking elements to O(log n) per operation. Overall, the approach remains efficient within the problem's constraints.
What Interviewers Usually Probe
- Ensure the candidate uses sorting as a first step to solve the problem.
- Look for the candidate's understanding of simulation and alternating turns.
- Check if the candidate can optimize using heaps or priority queues.
Common Pitfalls or Variants
Common pitfalls
- Forgetting to sort the array before simulating the game, which leads to incorrect answers.
- Improper simulation of the turns, such as not appending the elements in the correct order.
- Not using efficient data structures like heaps, resulting in higher time complexity.
Follow-up variants
- Modify the problem to have Alice and Bob pick elements based on a different rule, such as alternating picks instead of smallest-first.
- Change the number of players and adjust the game mechanics accordingly.
- Introduce a condition where players can pick from a specific range of numbers rather than from the entire sorted array.
How GhostInterview Helps
- GhostInterview provides step-by-step guidance for breaking down problems like this using sorting and simulation techniques.
- It helps candidates optimize their solutions with advanced data structures like heaps to meet performance requirements.
- The platform prepares you for common pitfalls by emphasizing the importance of sorting and correct simulation logic.
Topic Pages
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- Coding Interview Assistant - See how GhostInterview supports array, string, linked list, graph, and tree interview workflows.
- How GhostInterview Works - Review the screenshot, reasoning, and answer flow before using the solver in a live interview.
FAQ
What is the best approach for solving the Minimum Number Game?
Sorting the array initially and then simulating the turns with alternating picks between Alice and Bob is the best approach. Optionally, use heaps for optimization.
How do I handle the game simulation in the Minimum Number Game?
You simulate the game by alternating between Alice and Bob, where Alice always picks the smallest remaining number and Bob picks the next smallest.
What data structure should I use to optimize the solution for the Minimum Number Game?
Using a heap (priority queue) allows efficient removal of the smallest elements during each turn, reducing the time complexity of picking elements.
How do I ensure the correct order of elements in the result array for the Minimum Number Game?
After sorting the array, append Alice's and Bob's selections to the result array in the order they picked them during the game simulation.
What are the time and space complexities of the Minimum Number Game solution?
The time complexity is O(n log n) for sorting, with additional complexity for heap operations if used. The space complexity depends on the data structure used for simulation, typically O(n).
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