The quadratic sandwich(fedemagnani.github.io)
fedemagnani.github.io
The quadratic sandwich
https://fedemagnani.github.io/math/2026/04/08/the-quadratic-sandwich.html
16 comments
That's my article! Thank you so much to the user who posted it here <3
Thank you for writing this article! It really helped me clear up my understanding of why you care about min and max eigenvalues of a Hessian matrix, something I've been confused about for some time. I have https://fedemagnani.github.io/math/2025/07/04/fenchel.html queued up to read next (convex conjugates being another topic that confuse the hell out of me).
Haha that's great and excited to hear feedback, thank you so much! In these articles I deliberately want to keep a casual tone, just for grasping the concept, so probably a more rigorous material is very important as a follow-up
The animation is very good, making the article easy to understand
We studied it in our peparation for college entrance exams in India. Though the detail the article goes in is exhaustive. But I thought that this maybe common or almost common knowledge.
We used to call it sandwich theorem
The sandwich theorem would normally refer to this one: https://en.wikipedia.org/wiki/Squeeze_theorem
I immediately thought of the ham sandwich theorem
https://en.wikipedia.org/wiki/Ham_sandwich_theorem
https://en.wikipedia.org/wiki/Ham_sandwich_theorem
It frustrates me when math explainers, and textbooks, seem to start from the "here's why our methods are insufficient to solve our problem" and fail to provide an example of the problem they are trying to solve.
What's the question this method is attempting to answer? What does an answer look like? How does this method lead to it?
> If you have ever tried to minimize a function with gradient descent
"and if otherwise, go kick sand," I guess.
What's the question this method is attempting to answer? What does an answer look like? How does this method lead to it?
> If you have ever tried to minimize a function with gradient descent
"and if otherwise, go kick sand," I guess.
There is one very clear example that I ran across due to the reasons outlined in the article. If you have a wavelet and you're trying to slide it around to make it fit, that will fail spectacularly. There are lots of problems that boil down to basically the above.
The neural net answer is being able to spawn a wavelet at any position, as opposed to tweaking the position of an existing one.
The neural net answer is being able to spawn a wavelet at any position, as opposed to tweaking the position of an existing one.
This is a great article and its super helpful, thanks to whoever wrote it!
Simplex methods can handle those tough situations, though.
Kudos for beatiful formulae rendering.
I wonder what would happen with this analysis if a momentum term was added to the gradient descent. It seems that it would fix the specific failure modes in the examples, but I wonder if there's a corresponding mathematical way of categorizing what kinds of functions can(not) be quickly optimized with GD + momentum.