How do you solve Points That Intersect With Cars on LeetCode?

Points That Intersect With Cars (LeetCode #2848) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n log n) time complexity and O(1) space complexity. Key insight: Merging overlapping intervals is key to reducing the problem size.

What is the brute force solution for Points That Intersect With Cars?

The brute force approach for Points That Intersect With Cars is Brute Force, with O(n²) time complexity and O(n) space complexity. The brute-force approach involves checking every possible point on the number line to see if it falls within any car's parking range. This is straightforward but inefficient, especially as the number of cars increases. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Points That Intersect With Cars?

Points That Intersect With Cars uses the following concepts: Array, Hash Table, Prefix Sum. The recommended patterns to study are: Hash Map, Array.

What are the interview tips for Points That Intersect With Cars?

Always think about edge cases, such as completely overlapping intervals. Explain your thought process clearly while coding. Practice merging intervals as it's a common pattern in interviews.

What are common mistakes when solving Points That Intersect With Cars?

Not considering the case where cars overlap completely. Failing to account for the end point in the range.

#2848

Points That Intersect With Cars

Easy

ArrayHash TablePrefix SumHash MapArray

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

	Brute Force	Optimal Solution★
Time	O(n²)	O(n log n)
Space	O(n)	O(1)

💡

Intuition

Time O(n log n)Space O(1)

The optimal approach sorts the car ranges and merges overlapping intervals. This reduces the number of points we need to count by combining ranges, making it efficient.

⚙️

Algorithm

3 steps

1Step 1: Sort the car ranges by their starting points.
2Step 2: Initialize a variable to track the end of the last merged interval and a counter for covered points.
3Step 3: Iterate through the sorted ranges, merging them if they overlap, and count the total covered points.

solution.py12 lines

1def countCoveredPoints(nums):
2    nums.sort()
3    total_points = 0
4    current_end = -1
5    for start, end in nums:
6        if start > current_end:
7            total_points += end - start + 1
8            current_end = end
9        else:
10            total_points += max(0, end - current_end)
11            current_end = max(current_end, end)
12    return total_points

ℹ

Complexity note: The time complexity is O(n log n) due to the sorting step, while the space complexity is O(1) since we are using a constant amount of extra space for counting.

1Merging overlapping intervals is key to reducing the problem size.
2Sorting the intervals helps in efficiently determining overlaps.

Solutions and explanations are original Tejav content. Problem titles © LeetCode — use the LeetCode button above for the full problem statement.