Algorithm Analysis and Time Complexity

As a senior engineer with 7 years of experience in full-stack development and an interest in ML, you've likely encountered situations where the performance of an algorithm becomes crucial. Understanding the time complexity of an algorithm and its impact on the overall efficiency is essential for optimizing your code.

Time complexity refers to the amount of time taken by an algorithm to run as a function of the input size. It helps in determining how the algorithm performs as the input size grows. By analyzing the time complexity, you can gain insights into the scalability and efficiency of your code.

Let's consider an example to illustrate time complexity. Take the classic FizzBuzz problem, where you need to print numbers from 1 to 100 but replace multiples of 3 with 'Fizz' and multiples of 5 with 'Buzz'. All other numbers should be printed as is. Here is a Java implementation:

1<<code>>

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class Main {

  public static void main(String[] args) {

    // replace with your Java logic here

    for(int i = 1; i <= 100; i++) {

      if(i % 3 == 0 && i % 5 == 0) {

          System.out.println("FizzBuzz");

      } else if(i % 3 == 0) {

          System.out.println("Fizz");

      } else if(i % 5 == 0) {

          System.out.println("Buzz");

      } else {

          System.out.println(i);

      }

    }    

  }

}