LeetCode #2389 — EASY

Longest Subsequence With Limited Sum

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

The Problem

Problem Statement

You are given an integer array nums of length n, and an integer array queries of length m.

Return an array answer of length m where answer[i] is the maximum size of a subsequence that you can take from nums such that the sum of its elements is less than or equal to queries[i].

A subsequence is an array that can be derived from another array by deleting some or no elements without changing the order of the remaining elements.

Example 1:

Input: nums = [4,5,2,1], queries = [3,10,21]
Output: [2,3,4]
Explanation: We answer the queries as follows:
- The subsequence [2,1] has a sum less than or equal to 3. It can be proven that 2 is the maximum size of such a subsequence, so answer[0] = 2.
- The subsequence [4,5,1] has a sum less than or equal to 10. It can be proven that 3 is the maximum size of such a subsequence, so answer[1] = 3.
- The subsequence [4,5,2,1] has a sum less than or equal to 21. It can be proven that 4 is the maximum size of such a subsequence, so answer[2] = 4.

Example 2:

Input: nums = [2,3,4,5], queries = [1]
Output: [0]
Explanation: The empty subsequence is the only subsequence that has a sum less than or equal to 1, so answer[0] = 0.

Constraints:

n == nums.length
m == queries.length
1 <= n, m <= 1000
1 <= nums[i], queries[i] <= 10⁶

Step 01

Brute Force Baseline

Problem summary: You are given an integer array nums of length n, and an integer array queries of length m. Return an array answer of length m where answer[i] is the maximum size of a subsequence that you can take from nums such that the sum of its elements is less than or equal to queries[i]. A subsequence is an array that can be derived from another array by deleting some or no elements without changing the order of the remaining elements.

Baseline thinking

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

Pattern signal: Array · Binary Search · Greedy

Example 1

[4,5,2,1]
[3,10,21]

Example 2

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

Core Insight

What unlocks the optimal approach

Solve each query independently.
When solving a query, which elements of nums should you choose to make the subsequence as long as possible?
Choose the smallest elements in nums that add up to a sum less than the query.

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 #2389: Longest Subsequence With Limited Sum
class Solution {
    public int[] answerQueries(int[] nums, int[] queries) {
        Arrays.sort(nums);
        for (int i = 1; i < nums.length; ++i) {
            nums[i] += nums[i - 1];
        }
        int m = queries.length;
        int[] ans = new int[m];
        for (int i = 0; i < m; ++i) {
            int j = Arrays.binarySearch(nums, queries[i] + 1);
            ans[i] = j < 0 ? -j - 1 : j;
        }
        return ans;
    }
}

// Accepted solution for LeetCode #2389: Longest Subsequence With Limited Sum
func answerQueries(nums []int, queries []int) (ans []int) {
	sort.Ints(nums)
	for i := 1; i < len(nums); i++ {
		nums[i] += nums[i-1]
	}
	for _, q := range queries {
		ans = append(ans, sort.SearchInts(nums, q+1))
	}
	return
}

# Accepted solution for LeetCode #2389: Longest Subsequence With Limited Sum
class Solution:
    def answerQueries(self, nums: List[int], queries: List[int]) -> List[int]:
        nums.sort()
        s = list(accumulate(nums))
        return [bisect_right(s, q) for q in queries]

// Accepted solution for LeetCode #2389: Longest Subsequence With Limited Sum
impl Solution {
    pub fn answer_queries(mut nums: Vec<i32>, queries: Vec<i32>) -> Vec<i32> {
        nums.sort();

        for i in 1..nums.len() {
            nums[i] += nums[i - 1];
        }

        queries
            .iter()
            .map(|&q| match nums.binary_search(&q) {
                Ok(idx) => idx as i32 + 1,
                Err(idx) => idx as i32,
            })
            .collect()
    }
}

// Accepted solution for LeetCode #2389: Longest Subsequence With Limited Sum
function answerQueries(nums: number[], queries: number[]): number[] {
    nums.sort((a, b) => a - b);
    for (let i = 1; i < nums.length; i++) {
        nums[i] += nums[i - 1];
    }
    return queries.map(q => _.sortedIndex(nums, q + 1));
}

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 + m)

Space

O(n)

Approach Breakdown

LINEAR SCAN

O(n) time

O(1) space

Check every element from left to right until we find the target or exhaust the array. Each comparison is O(1), and we may visit all n elements, giving O(n). No extra space needed.

BINARY SEARCH

O(log n) time

O(1) space

Each comparison eliminates half the remaining search space. After k comparisons, the space is n/2ᵏ. We stop when the space is 1, so k = log₂ n. No extra memory needed — just two pointers (lo, hi).

Shortcut: Halving the input each step → O(log n). Works on any monotonic condition, not just sorted arrays.

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.

Boundary update without `+1` / `-1`

Wrong move: Setting `lo = mid` or `hi = mid` can stall and create an infinite loop.

Usually fails on: Two-element ranges never converge.

Fix: Use `lo = mid + 1` or `hi = mid - 1` where appropriate.

Using greedy without proof

Wrong move: Locally optimal choices may fail globally.

Usually fails on: Counterexamples appear on crafted input orderings.

Fix: Verify with exchange argument or monotonic objective before committing.