Sparse Ensemble Kalman Inversion

EnsembleKalmanProcesses.SparseInversion — Type

SparseInversion <: Process

A sparse ensemble Kalman Inversion process

Fields

γ::AbstractFloat
upper limit of l1-norm
threshold_value::AbstractFloat
threshold below which the norm of parameters is pruned to zero
uc_idx::Union{Colon, AbstractVector}
indices of parameters included in the evaluation of l1-norm constraint
reg::AbstractFloat
a small regularization value to enhance robustness of convex optimization

Constructors

SparseInversion()

defined at /home/runner/work/EnsembleKalmanProcesses.jl/EnsembleKalmanProcesses.jl/src/SparseEnsembleKalmanInversion.jl:20.

SparseInversion(γ, threshold_value, uc_idx, reg)

defined at /home/runner/work/EnsembleKalmanProcesses.jl/EnsembleKalmanProcesses.jl/src/SparseEnsembleKalmanInversion.jl:21.

SparseInversion(γ)

defined at /home/runner/work/EnsembleKalmanProcesses.jl/EnsembleKalmanProcesses.jl/src/SparseEnsembleKalmanInversion.jl:31.

source

EnsembleKalmanProcesses.sparse_eki_update — Function

 sparse_eki_update(
    ekp::EnsembleKalmanProcess{FT, IT, SparseInversion{FT}},
    u::AbstractMatrix{FT},
    g::AbstractMatrix{FT},
    y::AbstractMatrix{FT},
    obs_noise_cov::Union{AbstractMatrix{CT}, UniformScaling{CT}},
) where {FT <: Real, CT <: Real, IT}

Returns the sparse updated parameter vectors given their current values and the corresponding forward model evaluations, using the inversion algorithm from eqns. (3.7) and (3.14) of Schneider et al. (2021).

Localization is applied following Tong and Morzfeld (2022).

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EnsembleKalmanProcesses.sparse_qp — Function

sparse_qp(
    ekp::EnsembleKalmanProcess{FT, IT, SparseInversion{FT}},
    v_j::Vector{FT},
    cov_vv_inv::AbstractMatrix{FT},
    H_u::SparseArrays.SparseMatrixCSC{FT},
    H_g::SparseArrays.SparseMatrixCSC{FT},
    y_j::Vector{FT};
    H_uc::SparseArrays.SparseMatrixCSC{FT} = H_u,
) where {FT, IT}

Solving quadratic programming problem with sparsity constraint.

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