Local minima are rare in high dimensions because a strict local minimum has to curve upward in every direction, so all Hessian eigenvalues must be positive.
In a D-dimensional toy model where eigenvalue signs are independent, that’s a 2^(-D) event. In GOE-like random matrix models, positive definiteness is even rarer, roughly exp(-cD^2).
So as dimension grows, random critical points are much more likely to be saddles than minima. This is one reason high-dimensional optimization is often a saddle-escape problem, not a bad-local-minimum problem.
Wrote up some of the math here: https://t.co/vkaVqVD64N
I'd say this is the best of the first three books, what this means is that all the VFX spent in the upcoming movie will be spent on prior world building and not on the madness that ensues in this book
Great end-to-end read, good departure from gloomy hard sci-fi to once-in-a-while existential vibe with humor interspersed throughout. Kept listening to @mogwaiband intermittently while reading. Thank you @andyweirauthor for writing the book.