Why We Need Big O Notation?

The lecture discusses the limitations of using clock time to measure code performance due to hardware dependencies. It introduces Big O notation as a more reliable metric for evaluating algorithm efficiency, focusing on the number of operations rather than time. Practical examples using for and while loops demonstrate the variability of execution time across different systems. The lecture concludes by emphasizing the importance of Big O notation for performance rating and hints at future discussions on space complexity.

What is the significance of Big O notation in evaluating algorithms?

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Why is it not reliable to measure execution time in seconds or minutes?

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How do different hardware configurations affect the execution time of a program?

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What are the limitations of manually timing program execution?

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Explain how Big O notation provides a performance rating for code.

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What factors should be considered when analyzing the space complexity of an algorithm?

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In what ways does Big O notation differ from measuring execution time?

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