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Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics

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TLDR
In this article, an augmented Lagrangian method for the solution of variational problems is proposed. But this method is not suitable for continuous media and their mathematical modeling, such as viscoplasticity and elastoviscasticity.
Abstract
1. Some continuous media and their mathematical modeling 2. Variational formulations of the mechanical problems 3. Augmented Lagrangian methods for the solution of variational problems 4. Viscoplasticity and elastoviscoplasticity in small strains 5. Limit load analysis 6. Two-dimensional flow of incompressible viscoplastic fluids 7. Finite elasticity 8. Large displacement calculations of flexible rods References Index.

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A proximal multiplier method for separable convex minimization

TL;DR: In this paper, an inexact proximal multiplier method using proximal distances for solving convex minimization problems with a separable structure was proposed, which unifies the works of Chen and Teboulle (PCPM method), Kyono and Fukushima (NPCPMM), and Auslender and Teeboulle(EPDM).
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Explicit algorithms to solve a class of state constrained parabolic optimal control problems

TL;DR: In this article, an optimal control problem of a system governed by a linear parabolic equation with the following features: control is distributed, observation is either distributed or final, there are constraints on the state function and on its time derivative is considered.
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On the augmented Lagrangian approach to Signorini elastic contact problem

TL;DR: The Signorini problem is transformed into a saddle point problem of some augmented Lagrangian functional and then discretized by finite element methods to obtain optimal error estimates for general smooth domains which are not necessarily convex.
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New decomposition methods for solving variational inequality problems

TL;DR: In this paper, a decomposition method based on the Lagrange and augmented Lagrange mappings of the variational inequality problems is proposed, which solves the original problem via solving a series of small-scale problems, which may be much easier to solve than the original problems.
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Solving a non-smooth eigenvalue problem using operator-splitting methods

TL;DR: The Peaceman–Rachford scheme turns out to be superior to the Marchuk–Yanenko scheme in terms of accuracy and computational efficiency.
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