Introduction

Algorithmic challenges often involve intervals and can initially seem complex. One such problem is determining how many unique points are covered by a set of intervals on a number line. In this post, we’ll examine a solution to this problem and explore potential improvements.

Problem Statement

Given a number line with several intervals representing parked cars, where each interval [start, end] covers all integer points from start to end, the goal is to find out how many distinct integer points are covered by at least one car.

Example 1:

• Input: [[3, 6], [1, 5], [4, 7]]
• Explanation: The points 1, 2, 3, 4, 5, 6, and 7 are covered, so the answer is 7.

Example 2:

• Input: [[1, 3], [5, 8]]
• Explanation: The points 1, 2, 3, 5, 6, 7, and 8 are covered, resulting in 7 unique points.

Solution Approach

Here is a straightforward solution to the problem:

def numberOfPoints(self, nums: List[List[int]]) -> int:
    number_set = set()
    for elem in nums:
        for i in range(elem[0], elem[1] + 1):
            number_set.add(i)
    return len(number_set)

Explanation

1. Use of a Set: The solution utilizes a set, number_set, to store each unique point covered by the intervals. This is effective because sets automatically handle duplicate values, ensuring that each point is counted only once.
2. Iterating through Intervals: For each interval [start, end], the solution adds every integer point from start to end into the set.
3. Counting Unique Points: After processing all intervals, the length of the set gives the number of unique points covered.

Potential Improvements

While the solution is straightforward, there are ways to potentially improve it:

• Efficiency Considerations: The current solution may consume significant memory, especially when dealing with large intervals. An alternative approach could involve merging overlapping or adjacent intervals to reduce the number of distinct points tracked.
• Algorithmic Optimization: Instead of storing each point individually, an algorithm that merges intervals before counting the covered points could be more memory-efficient. By consolidating overlapping intervals into fewer ranges, we can simplify the counting process.
• Handling Large Intervals: For very large ranges or a high number of intervals, more sophisticated techniques, such as interval trees or sweep line algorithms, might be employed to manage and count the covered points more efficiently.