60 points by proghelper 1 year ago flag hide 16 comments
curious_learner 1 year ago next
Great article! I've been looking for a comprehensive guide to Big O notation, and this fits the bill. Any suggestions for resources on applying Big O analysis to real-world situations?
data_master 1 year ago next
There's an excellent course on Coursera called 'Algorithms, Part I' by Princeton. It has a great module on algorithmic efficiency, which includes real-world examples.
efficient_coder 1 year ago prev next
You can also check out 'Introduction to Algorithms' by Cormen, Leiserson, Rivest, and Stein, considered a classic textbook in the field. It covers everything you need, including practical examples.
numeric_lover 1 year ago prev next
This post is really eye-opening – I had no idea how important Big O notation is when it comes to writing efficient code. Thanks for sharing your knowledge!
clever_thinker 1 year ago next
I'm glad you found it helpful! Remembering to consider the efficiency of your code from the start can help prevent tons of headaches later on.
programming_beginner 1 year ago prev next
I have a quick question: why is Big O notation represented using capital 'O'? Is there a specific reason behind it?
notation_nerd 1 year ago next
That's a great question! Big O notation is named after the German mathematician Eduard 'BIG O' Heine, who did early work on the concept. Hence, we use the capital 'O' to represent the notation.
code_enthusiast 1 year ago prev next
I've noticed that some functions have multiple Big O representations like O(n) and O(n^2). When should I use one over the other?
algorithmic_expert 1 year ago next
Good question! It really depends on the function and the inputs. Basically, you should aim for the best-case scenario of the highest order. So if your function can be O(n) or O(n^2), you'd want to optimize for O(n).
newbie_programmer 1 year ago prev next
I stumbled upon an interesting article discussing how quantum computers might break Big O notation. Has anyone heard about this, and do you have any thoughts on it?
quantum_master 1 year ago next
Quantum computing certainly challenges the traditional Big O notation model, as it can provide exponential speed-ups. This is a rapidly growing field of study, and researchers are actively exploring its implications.
optimization_pro 1 year ago prev next
This might be a stretch, but do any well-known algorithms or data structures have lower bounds of O(log n)? If so, can you give me some examples?
data_struct_guru 1 year ago next
There sure are! Binary search trees, heaps, and balanced search trees like AVL and Red-Black trees all have lower bounds of O(log n). They're essential for efficient searching and sorting algorithms.
dev_in_training 1 year ago prev next
I'm trying to wrap my head around how to calculate Big O for recursive functions. Could someone point me in the right direction?
recursion_master 1 year ago next
When calculating Big O for recursive functions, watch for the base case when the recursion ends and the recursive case that repeats. The highest-order term is your Big O!
recursive_rockstar 1 year ago prev next
You might also find this helpful: when calculating Big O for nested loops, simply multiply the number of iterations for each loop. So O(n) with a single loop, O(n^2) with two nested loops, O(n^3) with three, etc.