LeetCode #2974 — EASY

Minimum Number Game

Build confidence with an intuition-first walkthrough focused on array fundamentals.

The Problem

Problem Statement

You are given a 0-indexed integer array nums of even length and there is also an empty array arr. Alice and Bob decided to play a game where in every round Alice and Bob will do one move. The rules of the game are as follows:

Every round, first Alice will remove the minimum element from nums, and then Bob does the same.
Now, first Bob will append the removed element in the array arr, and then Alice does the same.
The game continues until nums becomes empty.

Return the resulting array arr.

Example 1:

Input: nums = [5,4,2,3]
Output: [3,2,5,4]
Explanation: 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]
Explanation: 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

Step 01

Brute Force Baseline

Problem summary: You are given a 0-indexed integer array nums of even length and there is also an empty array arr. Alice and Bob decided to play a game where in every round Alice and Bob will do one move. The rules of the game are as follows: Every round, first Alice will remove the minimum element from nums, and then Bob does the same. Now, first Bob will append the removed element in the array arr, and then Alice does the same. The game continues until nums becomes empty. Return the resulting array arr.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: Array

Example 1

[5,4,2,3]

Example 2

[2,5]

Step 02

Core Insight

What unlocks the optimal approach

Sort the array in increasing order and then swap the adjacent elements.

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #2974: Minimum Number Game
class Solution {
    public int[] numberGame(int[] nums) {
        PriorityQueue<Integer> pq = new PriorityQueue<>();
        for (int x : nums) {
            pq.offer(x);
        }
        int[] ans = new int[nums.length];
        int i = 0;
        while (!pq.isEmpty()) {
            int a = pq.poll();
            ans[i++] = pq.poll();
            ans[i++] = a;
        }
        return ans;
    }
}

// Accepted solution for LeetCode #2974: Minimum Number Game
func numberGame(nums []int) (ans []int) {
	pq := &hp{nums}
	heap.Init(pq)
	for pq.Len() > 0 {
		a := heap.Pop(pq).(int)
		b := heap.Pop(pq).(int)
		ans = append(ans, b)
		ans = append(ans, a)
	}
	return
}

type hp struct{ sort.IntSlice }

func (h *hp) Less(i, j int) bool { return h.IntSlice[i] < h.IntSlice[j] }
func (h *hp) Pop() interface{} {
	old := h.IntSlice
	n := len(old)
	x := old[n-1]
	h.IntSlice = old[0 : n-1]
	return x
}
func (h *hp) Push(x interface{}) {
	h.IntSlice = append(h.IntSlice, x.(int))
}

# Accepted solution for LeetCode #2974: Minimum Number Game
class Solution:
    def numberGame(self, nums: List[int]) -> List[int]:
        heapify(nums)
        ans = []
        while nums:
            a, b = heappop(nums), heappop(nums)
            ans.append(b)
            ans.append(a)
        return ans

// Accepted solution for LeetCode #2974: Minimum Number Game
use std::cmp::Reverse;
use std::collections::BinaryHeap;

impl Solution {
    pub fn number_game(nums: Vec<i32>) -> Vec<i32> {
        let mut pq = BinaryHeap::new();

        for &x in &nums {
            pq.push(Reverse(x));
        }

        let mut ans = Vec::new();

        while let Some(Reverse(a)) = pq.pop() {
            if let Some(Reverse(b)) = pq.pop() {
                ans.push(b);
                ans.push(a);
            }
        }

        ans
    }
}

// Accepted solution for LeetCode #2974: Minimum Number Game
function numberGame(nums: number[]): number[] {
    const pq = new MinPriorityQueue<number>();
    for (const x of nums) {
        pq.enqueue(x);
    }
    const ans: number[] = [];
    while (pq.size()) {
        const a = pq.dequeue();
        const b = pq.dequeue();
        ans.push(b, a);
    }
    return ans;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n × log n)

Space

O(n)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.