Algorithmic complexity theory is an important part of computer science, but many developers are unaware of it. What does it mean when we say a function or an algorithm is efficient? How can we tell if we can do better? I help answer these questions and more with an overview and simple examples.

Complexity theory. Big-O. Constant, linear, logarithmic, and quadratic time versus space trade-offs. What does it actually mean when we say a function or an algorithm is efficient? How can we tell if we can do better? Join me on this tour through a corner of computer science few developers actively think about, and you’ll walk away with a new way of looking at code and thinking about problems.

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Philip Sharp at 14:45 on 2 Aug 2019

Really clear explanation of the concepts built up from simple ideas to more complex. Easy to follow and well-delivered.