LeetCode #986 — MEDIUM

Interval List Intersections

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

The Problem

Problem Statement

You are given two lists of closed intervals, firstList and secondList, where firstList[i] = [start_i, end_i] and secondList[j] = [start_j, end_j]. Each list of intervals is pairwise disjoint and in sorted order.

Return the intersection of these two interval lists.

A closed interval [a, b] (with a <= b) denotes the set of real numbers x with a <= x <= b.

The intersection of two closed intervals is a set of real numbers that are either empty or represented as a closed interval. For example, the intersection of [1, 3] and [2, 4] is [2, 3].

Example 1:

Input: firstList = [[0,2],[5,10],[13,23],[24,25]], secondList = [[1,5],[8,12],[15,24],[25,26]]
Output: [[1,2],[5,5],[8,10],[15,23],[24,24],[25,25]]

Example 2:

Input: firstList = [[1,3],[5,9]], secondList = []
Output: []

Constraints:

0 <= firstList.length, secondList.length <= 1000
firstList.length + secondList.length >= 1
0 <= start_i < end_i <= 10⁹
end_i < start_i+1
0 <= start_j < end_j <= 10⁹
end_j < start_j+1

Step 01

Brute Force Baseline

Problem summary: You are given two lists of closed intervals, firstList and secondList, where firstList[i] = [starti, endi] and secondList[j] = [startj, endj]. Each list of intervals is pairwise disjoint and in sorted order. Return the intersection of these two interval lists. A closed interval [a, b] (with a <= b) denotes the set of real numbers x with a <= x <= b. The intersection of two closed intervals is a set of real numbers that are either empty or represented as a closed interval. For example, the intersection of [1, 3] and [2, 4] is [2, 3].

Baseline thinking

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

Pattern signal: Array · Two Pointers

Example 1

[[0,2],[5,10],[13,23],[24,25]]
[[1,5],[8,12],[15,24],[25,26]]

Example 2

[[1,3],[5,9]]
[]

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 #986: Interval List Intersections
class Solution {
    public int[][] intervalIntersection(int[][] firstList, int[][] secondList) {
        List<int[]> ans = new ArrayList<>();
        int m = firstList.length, n = secondList.length;
        for (int i = 0, j = 0; i < m && j < n;) {
            int l = Math.max(firstList[i][0], secondList[j][0]);
            int r = Math.min(firstList[i][1], secondList[j][1]);
            if (l <= r) {
                ans.add(new int[] {l, r});
            }
            if (firstList[i][1] < secondList[j][1]) {
                ++i;
            } else {
                ++j;
            }
        }
        return ans.toArray(new int[ans.size()][]);
    }
}

// Accepted solution for LeetCode #986: Interval List Intersections
func intervalIntersection(firstList [][]int, secondList [][]int) [][]int {
	m, n := len(firstList), len(secondList)
	var ans [][]int
	for i, j := 0, 0; i < m && j < n; {
		l := max(firstList[i][0], secondList[j][0])
		r := min(firstList[i][1], secondList[j][1])
		if l <= r {
			ans = append(ans, []int{l, r})
		}
		if firstList[i][1] < secondList[j][1] {
			i++
		} else {
			j++
		}
	}
	return ans
}

# Accepted solution for LeetCode #986: Interval List Intersections
class Solution:
    def intervalIntersection(
        self, firstList: List[List[int]], secondList: List[List[int]]
    ) -> List[List[int]]:
        i = j = 0
        ans = []
        while i < len(firstList) and j < len(secondList):
            s1, e1, s2, e2 = *firstList[i], *secondList[j]
            l, r = max(s1, s2), min(e1, e2)
            if l <= r:
                ans.append([l, r])
            if e1 < e2:
                i += 1
            else:
                j += 1
        return ans

// Accepted solution for LeetCode #986: Interval List Intersections
impl Solution {
    pub fn interval_intersection(
        first_list: Vec<Vec<i32>>,
        second_list: Vec<Vec<i32>>,
    ) -> Vec<Vec<i32>> {
        let n = first_list.len();
        let m = second_list.len();
        let mut res = Vec::new();
        let (mut i, mut j) = (0, 0);
        while i < n && j < m {
            let start = first_list[i][0].max(second_list[j][0]);
            let end = first_list[i][1].min(second_list[j][1]);
            if start <= end {
                res.push(vec![start, end]);
            }
            if first_list[i][1] < second_list[j][1] {
                i += 1;
            } else {
                j += 1;
            }
        }
        res
    }
}

// Accepted solution for LeetCode #986: Interval List Intersections
function intervalIntersection(firstList: number[][], secondList: number[][]): number[][] {
    const n = firstList.length;
    const m = secondList.length;
    const res = [];
    let i = 0;
    let j = 0;
    while (i < n && j < m) {
        const start = Math.max(firstList[i][0], secondList[j][0]);
        const end = Math.min(firstList[i][1], secondList[j][1]);
        if (start <= end) {
            res.push([start, end]);
        }
        if (firstList[i][1] < secondList[j][1]) {
            i++;
        } else {
            j++;
        }
    }
    return res;
}

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 of elements. The outer loop picks one element, the inner loop scans the rest. For n elements that is n × (n−1)/2 comparisons = O(n²). No extra memory — just two loop variables.

TWO POINTERS

O(n) time

O(1) space

Each pointer traverses the array at most once. With two pointers moving inward (or both moving right), the total number of steps is bounded by n. Each comparison is O(1), giving O(n) overall. No auxiliary data structures are needed — just two index variables.

Shortcut: Two converging pointers on sorted data → O(n) time, O(1) space.

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.

Moving both pointers on every comparison

Wrong move: Advancing both pointers shrinks the search space too aggressively and skips candidates.

Usually fails on: A valid pair can be skipped when only one side should move.

Fix: Move exactly one pointer per decision branch based on invariant.