Book Review

When I discovered the first edition of Carl D. Meyer’s book on “Matrix Analysis and Applied Linear Algebra”, I considered it the best book on Linear Algebra I was aware of — and surely the best available book for an application-minded reader. In short: the book I really wished had been available when I was a graduate student and encountering this material for the first time.

So it was with great excitement and curiosity when I saw that a new, second edition was available. How could a near-perfect book be made even better?

A little while ago, I read (and quite enjoyed) “Learning eBPF” by Liz Rice. “Linux Observability with BPF” David Calavera and Lorenzo Fontana is another, slightly earlier, book on the BPF framework, also published by O’Reilly.

The extended Berkeley Packet Filter, or eBPF for short, is a plugin architecture for the Linux kernel. Using eBPF, it is possible to load (short) programs into the kernel at runtime, and have them be executed by the kernel. As the name suggests, the technology started out as a method to build custom filters for network packets, but it has since been extended and become much more general.

After seeing positive references to it on Linux Weekly News and elsewhere, I bought a copy of “Mastering Emacs” by Mickey Petersen.

In a moment of nostalgia, I picked up my copy of “The Art of UNIX Programming” by Eric S. Raymond (esr) and flipped through it again. It’s a book I’ve had since when it came out in 2004, and that I’ve always been quite fond of. I was looking forward to a review of “the way the future was”, as viewed from the early 2000s. So, it came as a bit of a surprise to me to find that the book seems to have aged rather poorly.

Analytic number theory is the application of methods from analysis to the study of integers, in particular primes. This may seem paradoxical: at the heart of analysis lie notions of continuity and differentiability — and what could be more discrete and discontinuous than the set of primes?

Linear Algebra is one of the foundational topics for all applied mathematics. But compared to Analysis, it initially often feels stranger and less familiar. Although technically not hard, the level of abstraction is higher, making it hard to see what all the formalism is supposed to achieve.