You already know optimization. In Chapter 3, we covered gradient ascent/descent, which lets us “climb hills” to find a maximum or minimum. Any optimization problem can be thought of as a version of hill climbing: we strive to find the best possible outcome out of a huge range of possibilities. The gradient ascent tool is simple and elegant, but it has an Achilles’ heel: it can lead us to find a peak that is only locally optimal, not globally optimal. In the hill-climbing analogy, it might take us to the top of a foothill, when going downhill for just a little while would enable us to start scaling the huge mountain ...
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