LeetCode #3026 — MEDIUM

Maximum Good Subarray Sum

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

The Problem

Problem Statement

You are given an array nums of length n and a positive integer k.

A subarray of nums is called good if the absolute difference between its first and last element is exactly k, in other words, the subarray nums[i..j] is good if |nums[i] - nums[j]| == k.

Return the maximum sum of a good subarray of nums. If there are no good subarrays, return 0.

Example 1:

Input: nums = [1,2,3,4,5,6], k = 1
Output: 11
Explanation: The absolute difference between the first and last element must be 1 for a good subarray. All the good subarrays are: [1,2], [2,3], [3,4], [4,5], and [5,6]. The maximum subarray sum is 11 for the subarray [5,6].

Example 2:

Input: nums = [-1,3,2,4,5], k = 3
Output: 11
Explanation: The absolute difference between the first and last element must be 3 for a good subarray. All the good subarrays are: [-1,3,2], and [2,4,5]. The maximum subarray sum is 11 for the subarray [2,4,5].

Example 3:

Input: nums = [-1,-2,-3,-4], k = 2
Output: -6
Explanation: The absolute difference between the first and last element must be 2 for a good subarray. All the good subarrays are: [-1,-2,-3], and [-2,-3,-4]. The maximum subarray sum is -6 for the subarray [-1,-2,-3].

Constraints:

2 <= nums.length <= 10⁵
-10⁹ <= nums[i] <= 10⁹
1 <= k <= 10⁹

Step 01

Brute Force Baseline

Problem summary: You are given an array nums of length n and a positive integer k. A subarray of nums is called good if the absolute difference between its first and last element is exactly k, in other words, the subarray nums[i..j] is good if |nums[i] - nums[j]| == k. Return the maximum sum of a good subarray of nums. If there are no good subarrays, return 0.

Baseline thinking

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

Pattern signal: Array · Hash Map

Example 1

[1,2,3,4,5,6]
1

Example 2

[-1,3,2,4,5]
3

Example 3

[-1,-2,-3,-4]
2

Core Insight

What unlocks the optimal approach

Save all the prefix sums into a HashMap.
For the index <code>i</code> store the element at index <code>i + 1</code> as the key and the prefix sum till <code>i</code> as the value.
For each prefix sum ending at <code>nums[i]</code>, try finding <code>nums[i] - k</code> and <code>nums[i] + k</code> in the HashMap and update the answer.

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 #3026: Maximum Good Subarray Sum
class Solution {
    public long maximumSubarraySum(int[] nums, int k) {
        Map<Integer, Long> p = new HashMap<>();
        p.put(nums[0], 0L);
        long s = 0;
        int n = nums.length;
        long ans = Long.MIN_VALUE;
        for (int i = 0; i < n; ++i) {
            s += nums[i];
            if (p.containsKey(nums[i] - k)) {
                ans = Math.max(ans, s - p.get(nums[i] - k));
            }
            if (p.containsKey(nums[i] + k)) {
                ans = Math.max(ans, s - p.get(nums[i] + k));
            }
            if (i + 1 < n && (!p.containsKey(nums[i + 1]) || p.get(nums[i + 1]) > s)) {
                p.put(nums[i + 1], s);
            }
        }
        return ans == Long.MIN_VALUE ? 0 : ans;
    }
}

// Accepted solution for LeetCode #3026: Maximum Good Subarray Sum
func maximumSubarraySum(nums []int, k int) int64 {
	p := map[int]int64{nums[0]: 0}
	var s int64 = 0
	n := len(nums)
	var ans int64 = math.MinInt64
	for i, x := range nums {
		s += int64(x)
		if t, ok := p[nums[i]-k]; ok {
			ans = max(ans, s-t)
		}
		if t, ok := p[nums[i]+k]; ok {
			ans = max(ans, s-t)
		}
		if i+1 == n {
			break
		}
		if t, ok := p[nums[i+1]]; !ok || s < t {
			p[nums[i+1]] = s
		}
	}
	if ans == math.MinInt64 {
		return 0
	}
	return ans
}

# Accepted solution for LeetCode #3026: Maximum Good Subarray Sum
class Solution:
    def maximumSubarraySum(self, nums: List[int], k: int) -> int:
        ans = -inf
        p = {nums[0]: 0}
        s, n = 0, len(nums)
        for i, x in enumerate(nums):
            s += x
            if x - k in p:
                ans = max(ans, s - p[x - k])
            if x + k in p:
                ans = max(ans, s - p[x + k])
            if i + 1 < n and (nums[i + 1] not in p or p[nums[i + 1]] > s):
                p[nums[i + 1]] = s
        return 0 if ans == -inf else ans

// Accepted solution for LeetCode #3026: Maximum Good Subarray Sum
// Rust example auto-generated from java reference.
// Replace the signature and local types with the exact LeetCode harness for this problem.
impl Solution {
    pub fn rust_example() {
        // Port the logic from the reference block below.
    }
}

// Reference (java):
// // Accepted solution for LeetCode #3026: Maximum Good Subarray Sum
// class Solution {
//     public long maximumSubarraySum(int[] nums, int k) {
//         Map<Integer, Long> p = new HashMap<>();
//         p.put(nums[0], 0L);
//         long s = 0;
//         int n = nums.length;
//         long ans = Long.MIN_VALUE;
//         for (int i = 0; i < n; ++i) {
//             s += nums[i];
//             if (p.containsKey(nums[i] - k)) {
//                 ans = Math.max(ans, s - p.get(nums[i] - k));
//             }
//             if (p.containsKey(nums[i] + k)) {
//                 ans = Math.max(ans, s - p.get(nums[i] + k));
//             }
//             if (i + 1 < n && (!p.containsKey(nums[i + 1]) || p.get(nums[i + 1]) > s)) {
//                 p.put(nums[i + 1], s);
//             }
//         }
//         return ans == Long.MIN_VALUE ? 0 : ans;
//     }
// }

// Accepted solution for LeetCode #3026: Maximum Good Subarray Sum
function maximumSubarraySum(nums: number[], k: number): number {
    const p: Map<number, number> = new Map();
    p.set(nums[0], 0);
    let ans: number = -Infinity;
    let s: number = 0;
    const n: number = nums.length;
    for (let i = 0; i < n; ++i) {
        s += nums[i];
        if (p.has(nums[i] - k)) {
            ans = Math.max(ans, s - p.get(nums[i] - k)!);
        }
        if (p.has(nums[i] + k)) {
            ans = Math.max(ans, s - p.get(nums[i] + k)!);
        }
        if (i + 1 < n && (!p.has(nums[i + 1]) || p.get(nums[i + 1])! > s)) {
            p.set(nums[i + 1], s);
        }
    }
    return ans === -Infinity ? 0 : 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(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.

Mutating counts without cleanup

Wrong move: Zero-count keys stay in map and break distinct/count constraints.

Usually fails on: Window/map size checks are consistently off by one.

Fix: Delete keys when count reaches zero.