scholarly journals Nonsmooth analysis and quasi-convexification in elastic energy minimization problems

1995 ◽  
Vol 10 (3-4) ◽  
pp. 217-221 ◽  
Author(s):  
Y. Grabovsky
2018 ◽  
Vol 10 (9) ◽  
pp. 8057-8065 ◽  
Author(s):  
Ja Yang Koo ◽  
Hyunguk Kwon ◽  
Minwoo Ahn ◽  
Mingi Choi ◽  
Ji-Won Son ◽  
...  

Author(s):  
Jorg H. Kappes ◽  
Bjoern Andres ◽  
Fred A. Hamprecht ◽  
Christoph Schnorr ◽  
Sebastian Nowozin ◽  
...  

2012 ◽  
Vol 61 ◽  
pp. 143-147 ◽  
Author(s):  
V.V. Likhanskii ◽  
T.N. Aliev ◽  
Mikhail Y. Kolesnik ◽  
I.A. Evdokimov ◽  
V.G. Zborovskii

1992 ◽  
Vol 291 ◽  
Author(s):  
Benoit Devincre ◽  
Vassilis Pontikis

ABSTRACTThe evolution of a random and initially homogeneous distribution of parallel and infinitely extended edge dislocations is studied by using elastic energy minimization without and in presence of a periodic external stress, τa. During the energy minimization without external stress (relaxation), randomly distributed dislocation dipoles are formed whereas, when the external stress is acting, the dislocations condense in walls. We investigated the spatial periodicity of this microstructure, λ, as a function of, τa, and of the total dislocation density. The elastic energy of the stress-induced microstructure is found to be comparable to the value obtained by relaxation. Thereby, emphasis is given to the dynamical character of patterning. A phenomenological model has been developed, explaining the correlation between λ and τa found in the simulations and comparing favorably with existing experimental data.


2021 ◽  
Vol 10 (1) ◽  
pp. 1154-1177
Author(s):  
Patrick L. Combettes ◽  
Lilian E. Glaudin

Abstract Various strategies are available to construct iteratively a common fixed point of nonexpansive operators by activating only a block of operators at each iteration. In the more challenging class of composite fixed point problems involving operators that do not share common fixed points, current methods require the activation of all the operators at each iteration, and the question of maintaining convergence while updating only blocks of operators is open. We propose a method that achieves this goal and analyze its asymptotic behavior. Weak, strong, and linear convergence results are established by exploiting a connection with the theory of concentrating arrays. Applications to several nonlinear and nonsmooth analysis problems are presented, ranging from monotone inclusions and inconsistent feasibility problems, to variational inequalities and minimization problems arising in data science.


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