The irony in the last two code optimization examples is that they can actually be anti-performant. "Premature optimization is the root of all evil" has long been the rallying cry by software engineers to avoid any thought of application performance until the very end of the software development cycle (at which point the optimization phase is typically ignored for economic/time-to-market reasons). It's also possible to use "premature optimization" as an excuse to justify writing bad code. Premature optimization is one habit that often stifles creativity in programming. But even if you see a performance improvement, return to the art side, and see whether the gain is worth the loss in readability and maintainability. Dismissing a strategic decision, as an example of premature optimization at a tactical level, is a grave mistake, and is probably the biggest criticism I have with Knuth’s quote. In his paper "Structured Programming with GoTo Statements", Donald Knuth, one of the eminent minds of computer science, coined the phrase "premature optimization is the root of all evil". For example, the team may use a spreadsheet model of the IoT system, examining factors such as frequency of messages, size of messages, number of units transmitting messages, and the total cost of the data usage. Explanation []. Here’s my story of learning to avoid premature optimization the hard way… Using Matlab's fminsearch and fminunc. the premature optimization definition xkcd why root java example evil optimization - When is optimisation premature? And the worst example of this is whenever I see someone re-implementing features from a standard library. Of course, since you’re doing benchmarks, you can prove or disprove that for your particular code. This statement is both lauded and demonized by programmers of all kinds of backgrounds and experience levels. Using Matlab's fmincon. This is especially true of those who have taken programming courses because they’ve had … Example: writing an O (n 2) O(n^2) O (n 2) algorithm to solve a problem when a simpler, mathematically correct, O (n) O(n) O (n) algorithm exists, simply because the simpler algorithm is harder to understand. Jan 21: Function Optimization Example Goal: Introduce you to a useful tool, MATLAB and its optimization subroutines, and show you how to use them on an example. As Knuth said, We should forget about small efficiencies, say about 97% of the time: premature optimization is the root of all evil. Using Matlab's fminsearch and fminunc, with desired posture. Strategic optimization, on the other hand, is extremely important, and decisions at a strategic or architectural level may have wide-ranging consequences. The real problem is that programmers have spent far too much time worrying about efficiency in the wrong places and at the wrong times; premature optimization is the root of all evil (or at least most of it) in programming. I can see how it could be a problem to write some convoluted optimization in the first place, but mostly I see premature optimization rearing its ugly head in fixing what ain't (known to be) broke. PREMATURE OPTIMIZATION First off, shout out to Will Ruth for pitching this idea to me. It’s a great example of how writing bad code might aftually be better in the long run. He’s a longtime reader and the moderator over at r/strongman on reddit, along with a fellow strongman competitor, coach, and decent dude in general. This early design time optimization may very well make the difference between a profitable business and a money losing endeavor. Donald Knuth once famously said:. 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