Preconditioning and globalizing conjugate gradients in dual space for quadratically penalized nonlinear-least squares problems
From MaRDI portal
Publication:1938916
DOI10.1007/s10589-012-9478-7zbMath1267.90093OpenAlexW2114197334WikidataQ58185693 ScholiaQ58185693MaRDI QIDQ1938916
Selime Gürol, Serge Gratton, Phillipe L. Toint
Publication date: 25 February 2013
Published in: Computational Optimization and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s10589-012-9478-7
preconditioningglobalizationdata assimilationtrust-region methodsconjugate-gradient methodsdual-space minimization
Related Items
A Levenberg-Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients, Numerical linear algebra in data assimilation, Observation thinning in data assimilation computations
Uses Software
Cites Work
- Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
- Data assimilation in weather forecasting: a case study in PDE-constrained optimization
- A stopping criterion for the conjugate gradient algorithm in a finite element method framework
- Accelerating the LSTRS Algorithm
- Algorithm 873
- Combination Preconditioning and the Bramble–Pasciak$^{+}$ Preconditioner
- Recipes for adjoint code construction
- Numerical Optimization
- Trust Region Methods
- Automatic Preconditioning by Limited Memory Quasi-Newton Updating
- Iterative Krylov Methods for Large Linear Systems
- Iterative Solution Methods
- Approximate Gauss–Newton Methods for Nonlinear Least Squares Problems
- The role of the inner product in stopping criteria for conjugate gradient iterations
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
