Space Complexity Calculator
Estimate memory usage of an algorithm based on its space complexity and input size. Whether you're solving coding interview problems or studying for exams, understanding how to convert algorithm + input size to memory usage estimate is essential.
How It Works
Conversion Logic:
For n elements of a given data type, compute bytes = spaceFunction(n) × bytesPerElement.
This is a fundamental skill tested in coding interviews, competitive programming, and CS theory exams.
Step-by-Step Examples
Example 1
Input: O(n) array of ints, n=1M
Output: ~4 MB
Explanation: 10^6 × 4 bytes = 4 MB
Example 2
Input: O(n²) matrix of ints, n=1000
Output: ~4 MB
Explanation: 10^6 × 4 bytes = 4 MB
Example 3
Input: O(2^n) subsets, n=20
Output: ~4 MB
Explanation: ~10^6 subsets × 4 bytes each
Practice Problems
Test your understanding with these exercises:
Interview tip: Be ready to explain the conversion process step-by-step on a whiteboard.
Implementation Code
python# Space Complexity Calculator implementation def convert(input_value): # Apply: For n elements of a given data type, compute bytes = spaceFunction(n) × bytesPerElement. pass # Implement the conversion logic
Frequently Asked Questions
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