The variational Kalman filter and an efficient implementation using limited memory BFGS

DOI10.1002/fld.2153zbMath1197.65213OpenAlexW2170227272MaRDI QIDQ2786298

Tuomo Kauranne, Heikki Haario, Johnathan M. Bardsley, Harri Auvinen

Publication date: 21 September 2010

Published in: International Journal for Numerical Methods in Fluids (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/fld.2153

zbMATH Keywords

optimizationlarge-scale optimizationnumerical examplesnonlinear dynamicsvariational methodsKalman filterBayesian inversionBroyden-Fletcher-Goldfarb-Shanno (LBFGS) methodquasi Newton methodssystem of linear stochastic difference equations

Mathematics Subject Classification ID

Numerical mathematical programming methods (65K05) Large-scale problems in mathematical programming (90C06) Nonlinear programming (90C30) Methods of quasi-Newton type (90C53) Stochastic difference equations (39A50) Numerical methods for difference equations (65Q10)

Related Items (7)

Optimal Low-rank Approximations of Bayesian Linear Inverse Problems ⋮ Design of the control set in the framework of variational data assimilation ⋮ Goal-Oriented Optimal Approximations of Bayesian Linear Inverse Problems ⋮ Krylov space approximate Kalman filtering ⋮ Ensemble smoothers for inference of hidden states and parameters in combinatorial regulatory model ⋮ Affine-mapping based variational ensemble Kalman filter ⋮ Stabilized BFGS approximate Kalman filter

Uses Software

• L-BFGS

Cites Work

• Representations of quasi-Newton matrices and their use in limited memory methods
• Updating Quasi-Newton Matrices with Limited Storage
• On Analysis Error Covariances in Variational Data Assimilation
• Variations on Variable-Metric Methods

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