The quadratic sandwich

The quadratic sandwich(fedemagnani.github.io)

147 points by mbustamanter 100 days ago | 15 comments

Great visualizations. Really enjoyed having a well-written example where mathematical proofs directly help with understanding a practical application.

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.

drunello 96 days ago |

That's my article! Thank you so much to the user who posted it here <3

f5129cac 96 days ago | |

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).

drunello 96 days ago | | |

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

xuzhenpeng 97 days ago |

The animation is very good, making the article easy to understand

Guestmodinfo 97 days ago | |

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

thaumasiotes 97 days ago | | |

The sandwich theorem would normally refer to this one: https://en.wikipedia.org/wiki/Squeeze_theorem

explainforwhat 96 days ago |

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.

Scene_Cast2 97 days ago |

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.

20k 96 days ago |

This is a great article and its super helpful, thanks to whoever wrote it!

CarVac 97 days ago |

Simplex methods can handle those tough situations, though.

FabHK 96 days ago | |

Simplex is not applicable. Simplex only minimises a linear function (f(x)=c'x) under linear inequality constraints (Ax≤b). The minimisation problem here is unconstrained, but (very) non-linear.

CarVac 95 days ago | | |

I guess I wasn't being precise, I meant Nelder-Mead.

vzaliva 97 days ago |

Kudos for beatiful formulae rendering.