Tootfinder

Opt-in global Mastodon full text search. Join the index!

No exact results. Similar results found.
@theodric@social.linux.pizza
2025-12-24 18:30:25

I made nog again

# My eggnog recipe (iterated over a synthesis of historic American recipes) Ingredients for about 1-1.5 liters of nog (depending on booze) - 6 yolks - Optional: 1-2 whites, beaten to stiff peaks - 100g sugar - 2x vanilla beans - About 0.75x nutmeg, grated fine - 1x 250ml small pot double cream - 750ml full-fat++ milk (6% is optimal) - Optional, but delicious: 500ml Napoleon brandy, white rum, vodka, or similar. Don't go too oaky. Destructions - Whisk egg yolks and sugar together until cream…
(DA NOG) -continuing...- - If using raw milk, hell yeah brother. Up to if you if you want to now pasturize the lot of it: hold at 72°C for a couple minutes, then put the whole pan in the freezer and chill, stirring every 30-45 minutes until desired temperature is reached - Optional: beat whites to stiff peaks, then fold through nog to add foamy fluffiness - Add booze to taste. Also perfectly drinkable without alcohol! Serves one, if I have anything to say about it.
@arXiv_mathOC_bot@mastoxiv.page
2025-11-14 09:35:40

An inexact semismooth Newton-Krylov method for semilinear elliptic optimal control problem
Shiqi Chen, Xuesong Chen
arxiv.org/abs/2511.10058 arxiv.org/pdf/2511.10058 arxiv.org/html/2511.10058
arXiv:2511.10058v1 Announce Type: new
Abstract: An inexact semismooth Newton method has been proposed for solving semi-linear elliptic optimal control problems in this paper. This method incorporates the generalized minimal residual (GMRES) method, a type of Krylov subspace method, to solve the Newton equations and utilizes nonmonotonic line search to adjust the iteration step size. The original problem is reformulated into a nonlinear equation through variational inequality principles and discretized using a second-order finite difference scheme. By leveraging slanting differentiability, the algorithm constructs semismooth Newton directions and employs GMRES method to inexactly solve the Newton equations, significantly reducing computational overhead. A dynamic nonmonotonic line search strategy is introduced to adjust stepsizes adaptively, ensuring global convergence while overcoming local stagnation. Theoretical analysis demonstrates that the algorithm achieves superlinear convergence near optimal solutions when the residual control parameter $\eta_k$ approaches to 0. Numerical experiments validate the method's accuracy and efficiency in solving semilinear elliptic optimal control problems, corroborating theoretical insights.
toXiv_bot_toot