Elastic-net regularization versus ℓ 1 -regularization for linear inverse problems with quasi-sparse solutions
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Publication:2971441
DOI10.1088/1361-6420/33/1/015004OpenAlexW3103262095MaRDI QIDQ2971441
Bernd Hofmann, Jun Zou, De-Han Chen
Publication date: 5 April 2017
Published in: Inverse Problems (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1604.03364
convergence rateslinear ill-posed problemssource conditionssparsity constraintselastic-net regularization\(\ell^1\)-regularization
Related Items (12)
On ℓ 1 -Regularization Under Continuity of the Forward Operator in Weaker Topologies ⋮ Generalized conditional gradient method for elastic-net regularization ⋮ A projected homotopy perturbation method for nonlinear inverse problems in Banach spaces ⋮ Numerical solution of time-dependent component with sparse structure of source term for a time fractional diffusion equation ⋮ Convergence rates of Tikhonov regularizations for elliptic and parabolic inverse radiativity problems ⋮ Convergence rates of Tikhonov regularization for recovering growth rates in a Lotka-Volterra competition model with diffusion ⋮ Variational source condition for ill-posed backward nonlinear Maxwell’s equations ⋮ $ \newcommand{\e}{{\rm e}} {\alpha\ell_{1}-\beta\ell_{2}}$ regularization for sparse recovery ⋮ Sparse inverse covariance matrix estimation via the $ \newcommand{\e}{{\rm e}} \ell_{0}$ -norm with Tikhonov regularization ⋮ Tikhonov regularization with \({\ell^{0}}\)-term complementing a convex penalty: \({\ell^{1}}\)-convergence under sparsity constraints ⋮ Variational source conditions for inverse Robin and flux problems by partial measurements ⋮ The two-point gradient methods for nonlinear inverse problems based on Bregman projections
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