Sidebar: Optimization in the real world

Optimization is a classic problem that has been studied by Newton and Gauss all the way up to mathematicians and engineers in the present day. Many optimization procedures are iterative; they can be thought of as taking a small step, checking our elevation and then taking another small uphill step until we reach a point from which there is no direction in which we can climb any higher.
The danger in this hill-climbing approach is that if the steps are too small, we may get stuck at one of the many local maxima in the foothills, which will not tell us the best set of controllable inputs. There are many techniques to avoid this problem, some based on statistics and spreading our bets widely, and others based on systems seen in nature, like biological evolution or the cooling of atoms in glass.

Optimization is a process we are all familiar with in our daily lives, even if we have never used algorithms like gradient descent or simulated annealing. A great image for optimization in the real world comes up in a recent TechZing podcast with the co-founders of data-mining competition platform Kaggle. One of the authors of this paper was explaining an iterative optimization technique, and the host says, "So, in a sense Jeremy, your approach was like that of doing a startup, which is just get something out there and iterate and iterate and iterate." The takeaway, whether you are a tiny startup or a giant insurance company, is that we unconsciously use optimization whenever we decide how to get to where we want to go.
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Источник: Jeremy Howard, Margit Zwemer, and Mike Loukides. Designing Great Data Products. 2012 {original}

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