scholarly journals Minimum-norm cortical source estimation in layered head models is robust against skull conductivity error

NeuroImage ◽  
2013 ◽  
Vol 81 ◽  
pp. 265-272 ◽  
Author(s):  
Matti Stenroos ◽  
Olaf Hauk
2001 ◽  
Vol 25 (4−2) ◽  
pp. 1123-1126
Author(s):  
S. Uchida ◽  
A. Tachikawa ◽  
K. Goto ◽  
K. Iramina ◽  
S. Ueno

2013 ◽  
Vol 32 (8) ◽  
pp. 170-182 ◽  
Author(s):  
Jorge Lopez-Moreno ◽  
Elena Garces ◽  
Sunil Hadap ◽  
Erik Reinhard ◽  
Diego Gutierrez

2020 ◽  
Vol 10 (1) ◽  
pp. 450-476
Author(s):  
Radu Ioan Boţ ◽  
Sorin-Mihai Grad ◽  
Dennis Meier ◽  
Mathias Staudigl

Abstract In this work we investigate dynamical systems designed to approach the solution sets of inclusion problems involving the sum of two maximally monotone operators. Our aim is to design methods which guarantee strong convergence of trajectories towards the minimum norm solution of the underlying monotone inclusion problem. To that end, we investigate in detail the asymptotic behavior of dynamical systems perturbed by a Tikhonov regularization where either the maximally monotone operators themselves, or the vector field of the dynamical system is regularized. In both cases we prove strong convergence of the trajectories towards minimum norm solutions to an underlying monotone inclusion problem, and we illustrate numerically qualitative differences between these two complementary regularization strategies. The so-constructed dynamical systems are either of Krasnoselskiĭ-Mann, of forward-backward type or of forward-backward-forward type, and with the help of injected regularization we demonstrate seminal results on the strong convergence of Hilbert space valued evolutions designed to solve monotone inclusion and equilibrium problems.


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