How do you solve Decompress Run-Length Encoded List on LeetCode?

Decompress Run-Length Encoded List (LeetCode #1313) can be solved using Brute Force or Optimal Solution. The optimal approach is Optimal Solution with O(n) time complexity and O(n) space complexity. Key insight: Understanding run-length encoding helps in visualizing the problem.

What is the brute force solution for Decompress Run-Length Encoded List?

The brute force approach for Decompress Run-Length Encoded List is Brute Force, with O(n²) time complexity and O(n) space complexity. The brute force approach involves iterating through the list and manually constructing the decompressed list by repeating each value according to its frequency. This is straightforward but inefficient for larger inputs. The optimal Optimal Solution approach improves this to O(n).

What algorithm or data structure is used to solve Decompress Run-Length Encoded List?

Decompress Run-Length Encoded List uses the following concepts: Array. The recommended patterns to study are: Array, Two Pointers.

What are the interview tips for Decompress Run-Length Encoded List?

Clarify the problem statement and constraints before coding. Think about the time and space complexity of your solution. Practice dry runs of your code with sample inputs to ensure correctness.

What are common mistakes when solving Decompress Run-Length Encoded List?

Not considering the size of the output list beforehand. Using nested loops unnecessarily, leading to inefficiency.

#1313

Decompress Run-Length Encoded List

Easy

ArrayArrayTwo Pointers

LeetCode ↗

Approaches

Brute ForceOptimal

Complexity Comparison

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

💡

Intuition

Time O(n)Space O(n)

The optimal solution uses a single pass through the input list while calculating the total size of the decompressed list first, then constructs it in a second pass. This avoids unnecessary repeated appending and improves efficiency.

⚙️

Algorithm

3 steps

1Step 1: Calculate the total size of the decompressed list by summing all frequencies.
2Step 2: Create an array of that size.
3Step 3: Fill the array by iterating through the input list, placing each value at the correct indices based on its frequency.

solution.py12 lines

1# Full working Python code
2
3def decompressRLElist(nums):
4    total_length = sum(nums[i] for i in range(0, len(nums), 2))
5    decompressed = [0] * total_length
6    index = 0
7    for i in range(0, len(nums), 2):
8        freq = nums[i]
9        val = nums[i + 1]
10        decompressed[index:index + freq] = [val] * freq
11        index += freq
12    return decompressed

ℹ

Complexity note: The time complexity is O(n) because we traverse the input list twice, and the space complexity is O(n) due to the storage of the decompressed list.

1Understanding run-length encoding helps in visualizing the problem.
2Recognizing the need for efficient list construction can lead to better solutions.

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