LeetCode #912 — MEDIUM

Sort an Array

Move from brute-force thinking to an efficient approach using array strategy.

The Problem

Problem Statement

Given an array of integers nums, sort the array in ascending order and return it.

You must solve the problem without using any built-in functions in O(nlog(n)) time complexity and with the smallest space complexity possible.

Example 1:

Input: nums = [5,2,3,1]
Output: [1,2,3,5]
Explanation: After sorting the array, the positions of some numbers are not changed (for example, 2 and 3), while the positions of other numbers are changed (for example, 1 and 5).

Example 2:

Input: nums = [5,1,1,2,0,0]
Output: [0,0,1,1,2,5]
Explanation: Note that the values of nums are not necessarily unique.

Constraints:

1 <= nums.length <= 5 * 10⁴
-5 * 10⁴ <= nums[i] <= 5 * 10⁴

Step 01

Brute Force Baseline

Problem summary: Given an array of integers nums, sort the array in ascending order and return it. You must solve the problem without using any built-in functions in O(nlog(n)) time complexity and with the smallest space complexity possible.

Baseline thinking

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

Pattern signal: Array

Example 1

[5,2,3,1]

Example 2

[5,1,1,2,0,0]

Step 02

Core Insight

What unlocks the optimal approach

No official hints in dataset. Start from constraints and look for a monotonic or reusable state.

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 #912: Sort an Array
class Solution {
    private int[] nums;

    public int[] sortArray(int[] nums) {
        this.nums = nums;
        quikcSort(0, nums.length - 1);
        return nums;
    }

    private void quikcSort(int l, int r) {
        if (l >= r) {
            return;
        }
        int x = nums[(l + r) >> 1];
        int i = l - 1, j = r + 1;
        while (i < j) {
            while (nums[++i] < x) {
            }
            while (nums[--j] > x) {
            }
            if (i < j) {
                int t = nums[i];
                nums[i] = nums[j];
                nums[j] = t;
            }
        }
        quikcSort(l, j);
        quikcSort(j + 1, r);
    }
}

// Accepted solution for LeetCode #912: Sort an Array
func sortArray(nums []int) []int {
	quickSort(nums, 0, len(nums)-1)
	return nums
}

func quickSort(nums []int, l, r int) {
	if l >= r {
		return
	}
	i, j := l-1, r+1
	x := nums[(l+r)>>1]
	for i < j {
		for {
			i++
			if nums[i] >= x {
				break
			}
		}
		for {
			j--
			if nums[j] <= x {
				break
			}
		}
		if i < j {
			nums[i], nums[j] = nums[j], nums[i]
		}
	}
	quickSort(nums, l, j)
	quickSort(nums, j+1, r)
}

# Accepted solution for LeetCode #912: Sort an Array
class Solution:
    def sortArray(self, nums: List[int]) -> List[int]:
        def quick_sort(l, r):
            if l >= r:
                return
            x = nums[randint(l, r)]
            i, j, k = l - 1, r + 1, l
            while k < j:
                if nums[k] < x:
                    nums[i + 1], nums[k] = nums[k], nums[i + 1]
                    i, k = i + 1, k + 1
                elif nums[k] > x:
                    j -= 1
                    nums[j], nums[k] = nums[k], nums[j]
                else:
                    k = k + 1
            quick_sort(l, i)
            quick_sort(j, r)

        quick_sort(0, len(nums) - 1)
        return nums

// Accepted solution for LeetCode #912: Sort an Array
impl Solution {
    pub fn sort_array(mut nums: Vec<i32>) -> Vec<i32> {
        let n = nums.len();
        Self::quick_sort(&mut nums, 0, n - 1);
        return nums;
    }

    fn quick_sort(nums: &mut Vec<i32>, left: usize, right: usize) {
        if left >= right {
            return;
        }
        let mut i = left as i32 - 1;
        let mut j = right as i32 + 1;
        let pivot = nums[left];
        while i < j {
            loop {
                i += 1;
                if nums[i as usize] >= pivot {
                    break;
                }
            }
            loop {
                j -= 1;
                if nums[j as usize] <= pivot {
                    break;
                }
            }
            if i < j {
                nums.swap(i as usize, j as usize);
            }
        }
        Self::quick_sort(nums, left, j as usize);
        Self::quick_sort(nums, j as usize + 1, right);
    }
}

// Accepted solution for LeetCode #912: Sort an Array
function sortArray(nums: number[]): number[] {
    function quickSort(l: number, r: number) {
        if (l >= r) {
            return;
        }
        let i = l - 1;
        let j = r + 1;
        const x = nums[(l + r) >> 1];
        while (i < j) {
            while (nums[++i] < x);
            while (nums[--j] > x);
            if (i < j) {
                [nums[i], nums[j]] = [nums[j], nums[i]];
            }
        }
        quickSort(l, j);
        quickSort(j + 1, r);
    }
    const n = nums.length;
    quickSort(0, n - 1);
    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)

Space

O(1)

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.