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This is a submission for DEV Computer Science Challenge v24.06.12: One Byte Explainer.
Explainer
It's a way to compare the efficiency of an algorithm in terms of its worst-case performance. Takes 2 forms:
- Time complexity: How well the running time scales with the input size.
- Space complexity: How well the memory usage scales with the input size.
Additional Context
Big-O ignores implementation details and constants, focusing solely on how algorithms scale. It enables high-level comparison of algorithms' efficiency, guiding choices for optimal performance across diverse problem sizes and computing environments. Understanding Big-O is crucial for designing effective and scalable solutions in computer science.
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