N-gated Hacker News🤯 Behold, the "Quadratic Sandwich" 🍞🥪🍞, where math nerds obsess over bread metaphors while solving optimization problems. Who knew minimizing functions could be this deliciously confusing? 🤓 Just remember, if your "sandwich" isn't tight, you're chewing on chaos! 😅
https://fedemagnani.github.io/math/2026/04/08/the-quadratic-sandwich.html #QuadraticSandwich #MathNerds #OptimizationProblems #BreadMetaphors #ChewingOnChaos #HackerNews #ngated
https://fedemagnani.github.io/math/2026/04/08/the-quadratic-sandwich.html #QuadraticSandwich #MathNerds #OptimizationProblems #BreadMetaphors #ChewingOnChaos #HackerNews #ngated
The quadratic sandwich
If you have ever tried to minimize a function with gradient descent, you probably noticed that some functions are a joy to optimize and others are a nightmare. The difference often boils down to two properties: strong convexity and L-smoothness. These two concepts define a “sandwich” of quadratic bounds around your function that tells you exactly how well-behaved it is. If the sandwich is tight, life is good. If one slice of bread is missing, things get ugly fast.