June 2016
Beginner to intermediate
398 pages
8h 51m
English
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Summary
Having all methods in mind that are presented in this book, one should be able to find the best optimizer for any given problem. To choose the optimizer, the dimensionality and complexity of the problem play a role as well as the shape of the function to optimize.
Numerical optimization methods were introduced in this chapter, but special care was given to stochastic methods to solve optimization problems. The advantages of traditional numerical optimization methods are in general that they are computationally efficient and reach an optima more quickly. The optim function in R has many of these methods implemented, and also the optimx and the ROI package are wrappers to many optimizers.
However, optima found by general-purpose numerical ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.