Nonlinear Schwarz preconditioning for nonlinear optimization problems with bound constraints
@article{Kothari2022NonlinearSP, title={Nonlinear Schwarz preconditioning for nonlinear optimization problems with bound constraints}, author={Hardik Kothari and Alena Kopanivc'akov'a and Rolf H. Krause}, journal={ArXiv}, year={2022}, volume={abs/2211.14780} }
We propose a nonlinear additive Schwarz method for solving nonlinear optimization problems with bound constraints. Our method is used as a “right-preconditioner" for solving the first-order optimality system arising within the sequential quadratic programming (SQP) framework using Newton’s method. The algorithmic scalability of this preconditioner is enhancedbyincorporating a solution-dependent coarse space, which takes into account the restricted constraints from the fine level. By means of…
References
SHOWING 1-10 OF 15 REFERENCES
Nonlinear Overlapping Domain Decomposition Methods
- Computer Science
- 2009
Some overlapping domain decomposition algorithms for solving sparse nonlinear system of equations arising from the discretization of partial differential equations, derived using Newton, Krylov and Schwarz are discussed.
On multilevel iterative methods for optimization problems
- MathematicsMath. Program.
- 1990
The construction of auxiliary problems as well as applications to elasto-plastic model and linear programming are described and the auxiliary problem for the dual of a perturbed linear program is interpreted as a dual of perturbed aggregated linear program.
Nonlinear Preconditioning: How to Use a Nonlinear Schwarz Method to Precondition Newton's Method
- Computer ScienceSIAM J. Sci. Comput.
- 2016
This paper obtains a preconditioner called RASPEN (Restricted Additive Schwarz Preconditioned Exact Newton) which is similar to ASPIN, but with all components directly defined by the iterative method, and has the advantage that RasPEN already converges when used as an iterative solver, in contrast to ASIN.
Inexact Newton Methods with Restricted Additive Schwarz Based Nonlinear Elimination for Problems with High Local Nonlinearity
- Computer Science, MathematicsSIAM J. Sci. Comput.
- 2011
This paper combines an inexact Newton method with a restricted additive Schwarz based nonlinear elimination and shows numerically that it performs well for solving the incompressible Navier-Stokes equations with high Reynolds numbers and on machines with large numbers of processors.
Convergence Rate Analysis of a Multiplicative Schwarz Method for Variational Inequalities
- Mathematics, Computer ScienceSIAM J. Numer. Anal.
- 2003
A linear convergence is derived for the Schwarz overlapping domain decomposition method when applied to constrained minimization problems and is presented to confirm the convergence estimate derived.
A recursive ℓ∞-trust-region method for bound-constrained nonlinear optimization
- Computer Science
- 2008
A recursive trust-region method is introduced for the solution of bound-cons-trained nonlinear nonconvex optimization problems for which a hierarchy of descriptions exists, which uses the infinity norm to define the shape of the trust region.
A Generalized Multigrid Method for Solving Contact Problems in Lagrange Multiplier based Unfitted Finite Element Method
- Computer ScienceComputer Methods in Applied Mechanics and Engineering
- 2022
An additive Schwarz method for variational inequalities
- MathematicsMath. Comput.
- 2000
The Schwarz domain decomposition method is proved to converge with a geometric rate depending on the decomposition of the domain based on an framework of convergence analysis established for general variational inequalities in Hilbert spaces.
Nonlinear Field-split Preconditioners for Solving Monolithic Phase-field Models of Brittle Fracture
- Computer ScienceComputer Methods in Applied Mechanics and Engineering
- 2023
Multilevel Active-Set Trust-Region (MASTR) Method for Bound Constrained Minimization
- Computer ScienceArXiv
- 2021
A novel variant of the recursive multilevel trust-region (RMTR) method, called MASTR, designed for solving non-convex bound-constrained minimization problems, which arise from the finite element discretization of partial differential equations.