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Visualizing Algorithmic Efficiency: A Detailed Look at Big O Notation(proghelper.com)

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.