1 code implementation • 30 Nov 2022 • Alexander Vidal, Samy Wu Fung, Luis Tenorio, Stanley Osher, Levon Nurbekyan
Instead of tuning $\alpha$, we repeatedly solve the optimization problem for a fixed $\alpha$ effectively performing a JKO update with a time-step $\alpha$.
no code implementations • 19 Aug 2022 • Eldad Haber, Moshe Eliasof, Luis Tenorio
In this paper we propose an alternative approach based on Maximum A-Posteriori (MAP) estimators, we name Maximum Recovery MAP (MR-MAP), to derive estimators that do not require the computation of the partition function, and reformulate the problem as an optimization problem.
no code implementations • 23 Feb 2017 • Julianne Chung, Matthias Chung, J. Tanner Slagel, Luis Tenorio
We describe stochastic Newton and stochastic quasi-Newton approaches to efficiently solve large linear least-squares problems where the very large data sets present a significant computational burden (e. g., the size may exceed computer memory or data are collected in real-time).