"Imagine you appear for an interview with the top giants. You have been asked a DSA problem, and you have somehow reached a perfect solution. Now, the interviewer asks, 'Could you please let me know the time and space complexity for this code?' After gaining experience in conducting interviews, I have observed a moment of confusion among DSA beginners when answering this question. They usually struggle to explain the time and space complexity of their code solution."
"If you're starting your DSA journey, these aren't just buzzwords - they are your best friends when it comes to writing efficient code. In this series, I'll walk you through how to calculate time and space complexity for any piece of code, from the simplest loops to more advanced recursive algorithms. Whether you're prepping for coding interviews or want to write cleaner, faster programs, this is the place to begin."
Time complexity measures how runtime grows as input size increases and focuses on the growth of operations rather than machine speed. Space complexity measures additional memory required relative to input size, including auxiliary data structures and recursion stack. Beginners frequently struggle to compute and explain both complexities during interviews. Common complexity classes include constant, logarithmic, linear, polynomial, and exponential. Techniques to determine complexity include counting dominant operations, analyzing loop nesting, solving recurrence relations for recursion, and ignoring constant factors and lower-order terms. Regular practice with iterative and recursive examples builds the ability to state and justify complexity clearly.
Read at Medium
Unable to calculate read time
Collection
[
|
...
]