The analysis of the augmented Lagrangian method for symmetric saddle point problem by generalized SVD

2018 ◽  
Author(s):  
Felicja Okulicka-Dłużewska
Keyword(s):  
Saddle Point ◽  
Augmented Lagrangian ◽  
Augmented Lagrangian Method ◽  
Saddle Point Problem ◽  
Lagrangian Method ◽  
Point Problem ◽  
Generalized Svd

scholarly journals A numerical solution to Monge’s problem with a Finsler distance as cost

2018 ◽  
Vol 52 (6) ◽  
pp. 2133-2148 ◽  
Author(s):  
Jean-David Benamou ◽  
Guillaume Carlier ◽  
Roméo Hatchi
Keyword(s):  
Saddle Point ◽  
Numerical Solution ◽  
Augmented Lagrangian ◽  
Optimal Transport ◽  
Saddle Point Problem ◽  
Point Problem ◽  
Finite Dimensional ◽  
Quadratic Cost ◽  
Augmented Lagrangian Approach ◽  
Well Posed

Monge’s problem with a Finsler cost is intimately related to an optimal ow problem. Discretization of this problem and its dual leads to a well-posed finite-dimensional saddle-point problem which can be solved numerically relatively easily by an augmented Lagrangian approach in the same spirit as the Benamou–Brenier method for the optimal transport problem with quadratic cost. Numerical results validate the method. We also emphasize that the algorithm only requires elementary operations and in particular never involves evaluation of the Finsler distance or of geodesics.

scholarly journals Computational aspects of the weak micro‐periodicity saddle point problem

PAMM ◽  
2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Ritukesh Bharali ◽  
Fredrik Larsson ◽  
Ralf Jänicke
Keyword(s):  
Saddle Point ◽  
Saddle Point Problem ◽  
Point Problem ◽  
Computational Aspects

scholarly journals An augmented Lagrangian method for solving total variation (TV)-based image registration model

2020 ◽  
Vol 14 ◽  
pp. 174830262097353
Author(s):  
Noppadol Chumchob ◽  
Ke Chen
Keyword(s):  
Image Registration ◽  
Augmented Lagrangian ◽  
Numerical Solutions ◽  
Augmented Lagrangian Method ◽  
Closed Form Solution ◽  
Numerical Algorithms ◽  
Lagrangian Method ◽  
Form Solution ◽  
The Other ◽  
Other Hand

Variational methods for image registration basically involve a regularizer to ensure that the resulting well-posed problem admits a solution. Different choices of regularizers lead to different deformations. On one hand, the conventional regularizers, such as the elastic, diffusion and curvature regularizers, are able to generate globally smooth deformations and generally useful for many applications. On the other hand, these regularizers become poor in some applications where discontinuities or steep gradients in the deformations are required. As is well-known, the total (TV) variation regularizer is more appropriate to preserve discontinuities of the deformations. However, it is difficult in developing an efficient numerical method to ensure that numerical solutions satisfy this requirement because of the non-differentiability and non-linearity of the TV regularizer. In this work we focus on computational challenges arising in approximately solving TV-based image registration model. Motivated by many efficient numerical algorithms in image restoration, we propose to use augmented Lagrangian method (ALM). At each iteration, the computation of our ALM requires to solve two subproblems. On one hand for the first subproblem, it is impossible to obtain exact solution. On the other hand for the second subproblem, it has a closed-form solution. To this end, we propose an efficient nonlinear multigrid (NMG) method to obtain an approximate solution to the first subproblem. Numerical results on real medical images not only confirm that our proposed ALM is more computationally efficient than some existing methods, but also that the proposed ALM delivers the accurate registration results with the desired property of the constructed deformations in a reasonable number of iterations.

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