big O notation
Big O Notation is a way to describe how the performance of an algorithm changes as the size of the input grows. It focuses on the worst-case scenario, giving a high-level understanding of the algorithm's efficiency. For example, if an algorithm is O(n), its time or space requirement increases linearly with the input size. If it's O(n²), the resource needs grow quadratically. By using Big O, we can compare different algorithms and determine which ones will be faster or more efficient as data scales up, providing valuable insight into their potential performance.
Additional Insights
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Big O notation is a way to evaluate the efficiency of an algorithm, specifically how its performance changes relative to the size of the input data. It describes the worst-case scenario for the time or space an algorithm requires as the input grows. For example, if an algorithm takes time proportional to the size of the input (like doubling the input size doubles the time), we say it operates in O(n) time. This notation helps compare different algorithms and understand their efficiency at scale, guiding choices in programming and system design.