scholarly journals Dual-Dual Formulation for a Contact Problem with Friction

2013 ◽  
Vol 16 (1) ◽  
pp. 1-16
Author(s):  
Michael Andres ◽  
Matthias Maischak ◽  
Ernst P. Stephan

AbstractA variational inequality formulation is derived for some frictional contact problems from linear elasticity. The formulation exhibits a two-fold saddle point structure and is of dual-dual type, involving the stress tensor as primary unknown as well as the friction force on the contact surface by means of a Lagrange multiplier. The approach starts with the minimization of the conjugate elastic potential. Applying Fenchel's duality theory to this dual minimization problem, the connection to the primal minimization problem and a dual saddle point problem is achieved. The saddle point problem possesses the displacement field and the rotation tensor as further unknowns. Introducing the friction force yields the dual-dual saddle point problem. The equivalence and unique solvability of both problems is shown with the help of the variational inequality formulations corresponding to the saddle point formulations, respectively.

PAMM ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ritukesh Bharali ◽  
Fredrik Larsson ◽  
Ralf Jänicke

2020 ◽  
Vol 60 (11) ◽  
pp. 1787-1809
Author(s):  
M. S. Alkousa ◽  
A. V. Gasnikov ◽  
D. M. Dvinskikh ◽  
D. A. Kovalev ◽  
F. S. Stonyakin

2013 ◽  
Vol 46 (3) ◽  
Author(s):  
Alicja Smoktunowicz ◽  
Felicja Okulicka-Dłużewska

AbstractNumerical stability of two main direct methods for solving the symmetric saddle point problem are analyzed. The first one is a generalization of Golub’s method for the augmented system formulation (ASF) and uses the Householder QR decomposition. The second method is supported by the singular value decomposition (SVD). Numerical comparison of some direct methods are given.


Acta Numerica ◽  
2013 ◽  
Vol 22 ◽  
pp. 509-575 ◽  
Author(s):  
Yurii Nesterov ◽  
Arkadi Nemirovski

In the past decade, problems related to l1/nuclear norm minimization have attracted much attention in the signal processing, machine learning and optimization communities. In this paper, devoted to l1/nuclear norm minimization as ‘optimization beasts’, we give a detailed description of two attractive first-order optimization techniques for solving problems of this type. The first one, aimed primarily at lasso-type problems, comprises fast gradient methods applied to composite minimization formulations. The second approach, aimed at Dantzig-selector-type problems, utilizes saddle-point first-order algorithms and reformulation of the problem of interest as a generalized bilinear saddle-point problem. For both approaches, we give complete and detailed complexity analyses and discuss the application domains.


OPSEARCH ◽  
2016 ◽  
Vol 53 (4) ◽  
pp. 917-933
Author(s):  
Maria C. Maciel ◽  
Sandra A. Santos ◽  
Graciela N. Sottosanto

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