Convergent least-squares optimization methods for variational data assimilation

Cites Work

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Adaptive cubic regularisation methods for unconstrained optimization. I: Motivation, convergence and numerical results
Conditioning and preconditioning of the variational data assimilation problem
Adaptive cubic regularisation methods for unconstrained optimization. II: Worst-case function- and derivative-evaluation complexity
Some numerical experiments with variable-storage quasi-Newton algorithms
On the limited memory BFGS method for large scale optimization
Data assimilation in weather forecasting: a case study in PDE-constrained optimization
The adjoint Newton algorithm for large-scale unconstrained optimization in meteorology applications
Best practices for comparing optimization algorithms
A truncated Newton optimization algorithm in meteorology applications with analytic Hessian/vector products
Convergence and complexity analysis of a Levenberg-Marquardt algorithm for inverse problems
Minimization of functions having Lipschitz continuous first partial derivatives
Numerical Experience with Limited-Memory Quasi-Newton and Truncated Newton Methods
Levenberg--Marquardt Methods Based on Probabilistic Gradient Models and Inexact Subproblem Solution, with Application to Data Assimilation
An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Inexact Newton Methods
An Analysis of a Hybrid Optimization Method for Variational Data Assimilation
Trust Region Methods
Deterministic Nonperiodic Flow
Nonlinear error dynamics for cycled data assimilation methods
Benchmarking Derivative-Free Optimization Algorithms
Approximate Gauss–Newton Methods for Nonlinear Least Squares Problems
Convergence Conditions for Ascent Methods
A method for the solution of certain non-linear problems in least squares

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