CompOpt(@comp_opt) 's Twitter Profile Photo

Ambrogio Maria Bernardelli presenting 'The BeMi stardust: a structured ensemble of binarized ', a joint work with Stefano Gualandi, Hoong Chuin Lau, and Simone Milanesi at in Nice

account_circle
Davide Duma(@duma_davide) 's Twitter Profile Photo

I am happy to be part of this young and enthusiastic research group founded by Stefano Gualandi at the Dept. of Mathematics of Università di Pavia. From today we also have a website with all our ongoing research projects and other activities!

account_circle
Pietro Belotti(@pietroBelotti) 's Twitter Profile Photo

As anticipated by Stefano Gualandi (thanks!), I'm looking for a postdoc to work at Politecnico di Milano on a solver for power network transmission/distribution problems with ACOPF constraints. All details here:

polimi.it/fileadmin/user…

Deadline (non-extendable!) is 20 December.

account_circle
Daria Vasyukova(@gereleth) 's Twitter Profile Photo

Stefano Gualandi What's the 'new school' way of doing this then? =)

My take was somewhat similar, although I used a lazy lower bound of 'cost so far + manhattan distance to finish'. Now I'm curious how much a better lower bound helps.
github.com/gereleth/aoc_p…

account_circle
Daria Vasyukova(@gereleth) 's Twitter Profile Photo

Stefano Gualandi Yes, I think that's what I did. Computed min cost of traveling from every cell to the finish point. And used 'cost so far + cost to finish' as lower bound for search states.
I didn't use networkx though, just made another priority queue for unconstrained movement =).

account_circle