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.

Why is Big O notation preferred over measuring time in seconds or minutes?

• It is faster to compute.

• It provides a consistent performance rating independent of hardware.

• It is easier to calculate.

• It is a more traditional method.

What problem is used as an example to demonstrate different solutions?

• Finding the maximum of an array

• Sorting a list

• Calculating factorial

• Finding the sum of N natural numbers

What is the main reason for the variability in execution time across different systems?

• Different hardware configurations

• Different operating systems

• Different software versions

• Different programming languages

Why can't we rely on system clock time for evaluating algorithm performance?

• It is outdated.

• It is not supported by all programming languages.

• It varies with different hardware and system conditions.

• It is too complex to measure.

What does Big O notation measure?

• The number of steps or operations

• The number of lines of code

• The memory usage in bytes

• The time taken in seconds

What is the main focus of Big O notation?

• The hardware specifications

• The software environment

• The user interface design

• The code's operations and comparisons

What is the purpose of using Big O notation in algorithm analysis?

• To determine the algorithm's memory usage

• To assess the algorithm's user-friendliness

• To evaluate the algorithm's efficiency in terms of steps

• To measure the exact time in seconds