How do you solve Video Stitching on LeetCode?

Video Stitching (LeetCode #1024) 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: Greedy algorithms often provide optimal solutions for interval covering problems.

What is the time complexity of Video Stitching?

The optimal solution for Video Stitching runs in O(n log n) time and uses O(1) space. The time complexity is dominated by the sorting step, which is O(n log n). The subsequent linear scan through the clips is O(n).

What is the brute force solution for Video Stitching?

The brute force approach for Video Stitching is Brute Force, with O(n²) time complexity and O(1) space complexity. The brute force approach involves generating all possible combinations of clips and checking if they can cover the entire time interval. This method is straightforward but inefficient, as it explores all possible subsets. The optimal Optimal Solution approach improves this to O(n log n).

What algorithm or data structure is used to solve Video Stitching?

Video Stitching uses the following concepts: Array, Dynamic Programming, Greedy. The recommended patterns to study are: Greedy, Sorting, Interval Scheduling.

What are the interview tips for Video Stitching?

Always clarify the problem requirements and constraints before diving into coding. Think about edge cases and test your solution against them. Explain your thought process clearly while coding; it helps interviewers understand your approach.

What are common mistakes when solving Video Stitching?

Failing to account for gaps in coverage, leading to incorrect assumptions about the number of clips needed. Not considering edge cases where clips do not cover the required time.

#1024

Video Stitching

Medium

ArrayDynamic ProgrammingGreedyGreedySortingInterval Scheduling

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

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

The optimal solution uses a greedy approach by sorting the clips based on their start times and then iteratively selecting the clip that extends the coverage the furthest. This ensures we use the minimum number of clips to cover the entire interval.

⚙️

Algorithm

3 steps

1Step 1: Sort the clips based on their start times.
2Step 2: Initialize variables for tracking the current end of coverage and the number of clips used.
3Step 3: Iterate through the sorted clips and extend coverage as far as possible with the fewest clips.

solution.py14 lines

1# Full working Python code
2def videoStitching(clips, time):
3    clips.sort(key=lambda x: x[0])
4    count, end, farthest = 0, 0, 0
5    i = 0
6    while end < time:
7        while i < len(clips) and clips[i][0] <= end:
8            farthest = max(farthest, clips[i][1])
9            i += 1
10        if end == farthest:
11            return -1
12        end = farthest
13        count += 1
14    return count

ℹ

Complexity note: The time complexity is dominated by the sorting step, which is O(n log n). The subsequent linear scan through the clips is O(n).

1Greedy algorithms often provide optimal solutions for interval covering problems.
2Sorting the intervals allows us to efficiently find the best clip to extend coverage.

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