Chapter 13 of Kevin Murphy's book Machine Learning: A Probabilistic Perspective discusses Sparse Linear Models. After a short introduction on the benefits of sparse models, he introduces the following problem:
How does he derive equation 13.1 above? i.e. why does it take that form, and what is $f$ supposed to represent here?