All three authors are large contributors to the field (the book _discrete and computational geometry_ by O'Rourke & Devadoss is excellent), Demaine has some origami in the collection at MoMA NYC^, Mitchell found a ptas for euclidian tsp (Google it - the paper is readable and there is another good write up of his vs Arora's)
Is any of the new machine learning tech promising here? I recall some new invariants of minimal surfaces were discovered only a few years ago by a DeepMind-made AI - and that's before LLMs. I'm wondering if AI can invent notions as powerful as something like homology groups: I imagine it could go about this by constructing lossy compressors that can still be used to accurately predict properties of geometric objects. That is indeed what homology groups and the like are for.
Does anyone know if this is still up-to-date?
All three authors are large contributors to the field (the book _discrete and computational geometry_ by O'Rourke & Devadoss is excellent), Demaine has some origami in the collection at MoMA NYC^, Mitchell found a ptas for euclidian tsp (Google it - the paper is readable and there is another good write up of his vs Arora's)
^ https://erikdemaine.org/curved/MoMA/
Are there any solutions similar to those found at https://www.cs.ru.nl/~freek/100/ ?
Is any of the new machine learning tech promising here? I recall some new invariants of minimal surfaces were discovered only a few years ago by a DeepMind-made AI - and that's before LLMs. I'm wondering if AI can invent notions as powerful as something like homology groups: I imagine it could go about this by constructing lossy compressors that can still be used to accurately predict properties of geometric objects. That is indeed what homology groups and the like are for.