Inverse Problems · 2005 · 164 citations · 15 references
EngineeringVariational AnalysisRegularization (Mathematics)Convex OptimizationMaximum Entropy RegularizationTotal Variation RegularizationInverse ProblemsFunctional AnalysisNonlinear Functional AnalysisIll-posed ProblemsApproximation TheoryConvergence AnalysisIll-posed Linear Equations
This paper deals with quantitative aspects of regularization for ill-posed linear equations in Banach spaces, when the regularization is done using a general convex penalty functional. The error estimates shown here by means of Bregman distances yield better convergences rates than those already known for maximum entropy regularization, as well as for total variation regularization.
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