LeetCode #2574 — EASY

Left and Right Sum Differences

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

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The Problem

Problem Statement

You are given a 0-indexed integer array nums of size n.

Define two arrays leftSum and rightSum where:

leftSum[i] is the sum of elements to the left of the index i in the array nums. If there is no such element, leftSum[i] = 0.
rightSum[i] is the sum of elements to the right of the index i in the array nums. If there is no such element, rightSum[i] = 0.

Return an integer array answer of size n where answer[i] = |leftSum[i] - rightSum[i]|.

Example 1:

Input: nums = [10,4,8,3]
Output: [15,1,11,22]
Explanation: The array leftSum is [0,10,14,22] and the array rightSum is [15,11,3,0].
The array answer is [|0 - 15|,|10 - 11|,|14 - 3|,|22 - 0|] = [15,1,11,22].

Example 2:

Input: nums = [1]
Output: [0]
Explanation: The array leftSum is [0] and the array rightSum is [0].
The array answer is [|0 - 0|] = [0].

Constraints:

1 <= nums.length <= 1000
1 <= nums[i] <= 10⁵

Step 01

Brute Force Baseline

Problem summary: You are given a 0-indexed integer array nums of size n. Define two arrays leftSum and rightSum where: leftSum[i] is the sum of elements to the left of the index i in the array nums. If there is no such element, leftSum[i] = 0. rightSum[i] is the sum of elements to the right of the index i in the array nums. If there is no such element, rightSum[i] = 0. Return an integer array answer of size n where answer[i] = |leftSum[i] - rightSum[i]|.

Baseline thinking

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

Pattern signal: Array

Example 1

[10,4,8,3]

Example 2

[1]

Core Insight

What unlocks the optimal approach

For each index i, maintain two variables leftSum and rightSum.
Iterate on the range j: [0 … i - 1] and add nums[j] to the leftSum and similarly iterate on the range j: [i + 1 … nums.length - 1] and add nums[j] to the rightSum.

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 #2574: Left and Right Sum Differences
class Solution {
    public int[] leftRigthDifference(int[] nums) {
        int left = 0, right = Arrays.stream(nums).sum();
        int n = nums.length;
        int[] ans = new int[n];
        for (int i = 0; i < n; ++i) {
            right -= nums[i];
            ans[i] = Math.abs(left - right);
            left += nums[i];
        }
        return ans;
    }
}

// Accepted solution for LeetCode #2574: Left and Right Sum Differences
func leftRigthDifference(nums []int) (ans []int) {
	var left, right int
	for _, x := range nums {
		right += x
	}
	for _, x := range nums {
		right -= x
		ans = append(ans, abs(left-right))
		left += x
	}
	return
}

func abs(x int) int {
	if x < 0 {
		return -x
	}
	return x
}

# Accepted solution for LeetCode #2574: Left and Right Sum Differences
class Solution:
    def leftRigthDifference(self, nums: List[int]) -> List[int]:
        left, right = 0, sum(nums)
        ans = []
        for x in nums:
            right -= x
            ans.append(abs(left - right))
            left += x
        return ans

// Accepted solution for LeetCode #2574: Left and Right Sum Differences
impl Solution {
    pub fn left_rigth_difference(nums: Vec<i32>) -> Vec<i32> {
        let mut left = 0;
        let mut right = nums.iter().sum::<i32>();
        nums.iter()
            .map(|v| {
                right -= v;
                let res = (left - right).abs();
                left += v;
                res
            })
            .collect()
    }
}

// Accepted solution for LeetCode #2574: Left and Right Sum Differences
function leftRigthDifference(nums: number[]): number[] {
    let left = 0,
        right = nums.reduce((a, b) => a + b);
    const ans: number[] = [];
    for (const x of nums) {
        right -= x;
        ans.push(Math.abs(left - right));
        left += x;
    }
    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)

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.