LeetCode #3264 — EASY

Final Array State After K Multiplication Operations I

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

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The Problem

Problem Statement

You are given an integer array nums, an integer k, and an integer multiplier.

You need to perform k operations on nums. In each operation:

Find the minimum value x in nums. If there are multiple occurrences of the minimum value, select the one that appears first.
Replace the selected minimum value x with x * multiplier.

Return an integer array denoting the final state of nums after performing all k operations.

Example 1:

Input: nums = [2,1,3,5,6], k = 5, multiplier = 2

Output: [8,4,6,5,6]

Explanation:

Operation	Result
After operation 1	[2, 2, 3, 5, 6]
After operation 2	[4, 2, 3, 5, 6]
After operation 3	[4, 4, 3, 5, 6]
After operation 4	[4, 4, 6, 5, 6]
After operation 5	[8, 4, 6, 5, 6]

Example 2:

Input: nums = [1,2], k = 3, multiplier = 4

Output: [16,8]

Explanation:

Operation	Result
After operation 1	[4, 2]
After operation 2	[4, 8]
After operation 3	[16, 8]

Constraints:

1 <= nums.length <= 100
1 <= nums[i] <= 100
1 <= k <= 10
1 <= multiplier <= 5

Step 01

Brute Force Baseline

Problem summary: You are given an integer array nums, an integer k, and an integer multiplier. You need to perform k operations on nums. In each operation: Find the minimum value x in nums. If there are multiple occurrences of the minimum value, select the one that appears first. Replace the selected minimum value x with x * multiplier. Return an integer array denoting the final state of nums after performing all k operations.

Baseline thinking

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

Pattern signal: Array · Math

Example 1

[2,1,3,5,6]
5
2

Example 2

[1,2]
3
4

Step 02

Core Insight

What unlocks the optimal approach

Maintain sorted pairs <code>(nums[index], index)</code> in a priority queue.
Simulate the operation <code>k</code> times.

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 #3264: Final Array State After K Multiplication Operations I
class Solution {
    public int[] getFinalState(int[] nums, int k, int multiplier) {
        PriorityQueue<Integer> pq
            = new PriorityQueue<>((i, j) -> nums[i] - nums[j] == 0 ? i - j : nums[i] - nums[j]);
        for (int i = 0; i < nums.length; i++) {
            pq.offer(i);
        }
        while (k-- > 0) {
            int i = pq.poll();
            nums[i] *= multiplier;
            pq.offer(i);
        }
        return nums;
    }
}

// Accepted solution for LeetCode #3264: Final Array State After K Multiplication Operations I
func getFinalState(nums []int, k int, multiplier int) []int {
	h := &hp{nums: nums}
	for i := range nums {
		heap.Push(h, i)
	}

	for k > 0 {
		i := heap.Pop(h).(int)
		nums[i] *= multiplier
		heap.Push(h, i)
		k--
	}

	return nums
}

type hp struct {
	sort.IntSlice
	nums []int
}

func (h *hp) Less(i, j int) bool {
	if h.nums[h.IntSlice[i]] == h.nums[h.IntSlice[j]] {
		return h.IntSlice[i] < h.IntSlice[j]
	}
	return h.nums[h.IntSlice[i]] < h.nums[h.IntSlice[j]]
}

func (h *hp) Pop() any {
	old := h.IntSlice
	n := len(old)
	x := old[n-1]
	h.IntSlice = old[:n-1]
	return x
}

func (h *hp) Push(x any) {
	h.IntSlice = append(h.IntSlice, x.(int))
}

# Accepted solution for LeetCode #3264: Final Array State After K Multiplication Operations I
class Solution:
    def getFinalState(self, nums: List[int], k: int, multiplier: int) -> List[int]:
        pq = [(x, i) for i, x in enumerate(nums)]
        heapify(pq)
        for _ in range(k):
            _, i = heappop(pq)
            nums[i] *= multiplier
            heappush(pq, (nums[i], i))
        return nums

// Accepted solution for LeetCode #3264: Final Array State After K Multiplication Operations I
/**
 * [3264] Final Array State After K Multiplication Operations I
 */
pub struct Solution {}

// submission codes start here

impl Solution {
    pub fn get_final_state(nums: Vec<i32>, k: i32, multiplier: i32) -> Vec<i32> {
        let mut nums = nums;

        for _ in 0..k {
            let min_value = nums.iter_mut().min().unwrap();
            *min_value = *min_value * multiplier;
        }

        nums
    }
}

// submission codes end

#[cfg(test)]
mod tests {
    use super::*;

    #[test]
    fn test_3264() {
        assert_eq!(
            vec![8, 4, 6, 5, 6],
            Solution::get_final_state(vec![2, 1, 3, 5, 6], 5, 2)
        );
        assert_eq!(vec![16, 8], Solution::get_final_state(vec![1, 2], 3, 4));
    }
}

// Accepted solution for LeetCode #3264: Final Array State After K Multiplication Operations I
function getFinalState(nums: number[], k: number, multiplier: number): number[] {
    const pq = new PriorityQueue<number>((i, j) =>
        nums[i] === nums[j] ? i - j : nums[i] - nums[j],
    );

    for (let i = 0; i < nums.length; ++i) {
        pq.enqueue(i);
    }
    while (k--) {
        const i = pq.dequeue()!;
        nums[i] *= multiplier;
        pq.enqueue(i);
    }
    return nums;
}

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 + k)

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.

Overflow in intermediate arithmetic

Wrong move: Temporary multiplications exceed integer bounds.

Usually fails on: Large inputs wrap around unexpectedly.

Fix: Use wider types, modular arithmetic, or rearranged operations.