> But if you can't do Big-O analysis, there are problems where you will be stuck. Your code will be running slowly and you won't know why, and all the micro-opt

> But if you can't do Big-O analysis, there are problems where you will be stuck. Your code will be running slowly and you won't know why, and all the micro-optimizations in the world can't make a O(n^2) algorithm run faster than an O(n) algorithm on even a moderately large data set. This may be a result of the domains I have worked in, but I have never encountered a situation where I could change the order-of-growth on an algorithm. In fact, most algorithms I have worked with have been defined by non-computational performance. I don't know formal complexity analysis, and personally think the resolution of Big-O is far too coarse for practical analysis anyway. I can still look at any piece of code and give you approximate polynomials for its runtime, memory requirements, and other computational features.