In this chapter, we have discussed several topics around optimization, such as general issues for optimization problems, expressing various kinds of optimization problems as LPPs, and quadratic optimization. Several examples were offered to make our discussion more practice-oriented, such as how to choose an optimal stock portfolio, optimize wealth and resources to promote sustainable development, and how much the government really should tax. In addition, we introduced several packages for optimization in R, Python, Julia, and Octave.
In the next chapter, we will discuss unsupervised learning. In particular, we will explain hierarchical clustering and k-means clustering. For R and Python, we will explain in detail several related ...
