LeetCode #2763 — HARD

Sum of Imbalance Numbers of All Subarrays

Break down a hard problem into reliable checkpoints, edge-case handling, and complexity trade-offs.

Solve on LeetCode
The Problem

Problem Statement

The imbalance number of a 0-indexed integer array arr of length n is defined as the number of indices in sarr = sorted(arr) such that:

  • 0 <= i < n - 1, and
  • sarr[i+1] - sarr[i] > 1

Here, sorted(arr) is the function that returns the sorted version of arr.

Given a 0-indexed integer array nums, return the sum of imbalance numbers of all its subarrays.

A subarray is a contiguous non-empty sequence of elements within an array.

Example 1:

Input: nums = [2,3,1,4]
Output: 3
Explanation: There are 3 subarrays with non-zero imbalance numbers:
- Subarray [3, 1] with an imbalance number of 1.
- Subarray [3, 1, 4] with an imbalance number of 1.
- Subarray [1, 4] with an imbalance number of 1.
The imbalance number of all other subarrays is 0. Hence, the sum of imbalance numbers of all the subarrays of nums is 3.

Example 2:

Input: nums = [1,3,3,3,5]
Output: 8
Explanation: There are 7 subarrays with non-zero imbalance numbers:
- Subarray [1, 3] with an imbalance number of 1.
- Subarray [1, 3, 3] with an imbalance number of 1.
- Subarray [1, 3, 3, 3] with an imbalance number of 1.
- Subarray [1, 3, 3, 3, 5] with an imbalance number of 2. 
- Subarray [3, 3, 3, 5] with an imbalance number of 1. 
- Subarray [3, 3, 5] with an imbalance number of 1.
- Subarray [3, 5] with an imbalance number of 1.
The imbalance number of all other subarrays is 0. Hence, the sum of imbalance numbers of all the subarrays of nums is 8.

Constraints:

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

Roadmap

  1. Brute Force Baseline
  2. Core Insight
  3. Algorithm Walkthrough
  4. Edge Cases
  5. Full Annotated Code
  6. Interactive Study Demo
  7. Complexity Analysis
Step 01

Brute Force Baseline

Problem summary: The imbalance number of a 0-indexed integer array arr of length n is defined as the number of indices in sarr = sorted(arr) such that: 0 <= i < n - 1, and sarr[i+1] - sarr[i] > 1 Here, sorted(arr) is the function that returns the sorted version of arr. Given a 0-indexed integer array nums, return the sum of imbalance numbers of all its subarrays. A subarray is a contiguous non-empty sequence of elements within an array.

Baseline thinking

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

Pattern signal: Array · Hash Map

Example 1

[2,3,1,4]

Example 2

[1,3,3,3,5]

Related Problems

  • Count Subarrays With Median K (count-subarrays-with-median-k)
Step 02

Core Insight

What unlocks the optimal approach

  • Iterate over all subarrays in a nested fashion. Namely, for each left endpoint, start from nums[left] and add elements nums[left + 1], nums[left + 2], etc.
  • To keep track of the imbalance value, maintain a set of added elements.
  • Increment the imbalance value whenever a new number is not adjacent (+/- 1) to other old numbers. For example, when you add 3 to [1, 5], or when you add 5 to [1, 3]. For a formal proof, consider three cases: new value is (i) largest, (ii) smallest, (iii) between two old numbers.
  • Decrement the imbalance value whenever a new number is adjacent (+/- 1) to two old numbers. For example, when you add 3 to [2, 4]. The imbalance value does not change in the case of one adjacent old number.
Interview move: turn each hint into an invariant you can check after every iteration/recursion step.
Step 03

Algorithm Walkthrough

Iteration Checklist

  1. Define state (indices, window, stack, map, DP cell, or recursion frame).
  2. Apply one transition step and update the invariant.
  3. Record answer candidate when condition is met.
  4. 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
Largest constraint values
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 #2763: Sum of Imbalance Numbers of All Subarrays
class Solution {
    public int sumImbalanceNumbers(int[] nums) {
        int n = nums.length;
        int ans = 0;
        for (int i = 0; i < n; ++i) {
            TreeMap<Integer, Integer> tm = new TreeMap<>();
            int cnt = 0;
            for (int j = i; j < n; ++j) {
                Integer k = tm.ceilingKey(nums[j]);
                if (k != null && k - nums[j] > 1) {
                    ++cnt;
                }
                Integer h = tm.floorKey(nums[j]);
                if (h != null && nums[j] - h > 1) {
                    ++cnt;
                }
                if (h != null && k != null && k - h > 1) {
                    --cnt;
                }
                tm.merge(nums[j], 1, Integer::sum);
                ans += cnt;
            }
        }
        return ans;
    }
}
Step 06

Interactive Study Demo

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

Press Step or Run All to begin.
Step 07

Complexity Analysis

Time
O(n^2 × 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.

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.

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