Solution of ill-posed nonconvex optimization problems with accuracy proportional to the error in input data
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Publication:2416755
DOI10.1134/S0965542518110064zbMath1412.90124OpenAlexW2903517565MaRDI QIDQ2416755
Publication date: 24 May 2019
Published in: Computational Mathematics and Mathematical Physics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1134/s0965542518110064
Hilbert spaceMinkowski functionalerrorgradient projection methodaccuracy estimateconvex closed setTikhonov's schemeill-posed optimization problem
Numerical mathematical programming methods (65K05) Nonconvex programming, global optimization (90C26) Programming in abstract spaces (90C48)
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Cites Work
- Reduction of variational inequalities with irregular operators on a ball to regular operator equations
- Iterative regularization methods for nonlinear ill-posed problems
- Convergence rate estimates for Tikhonov's scheme as applied to ill-posed nonconvex optimization problems
- On possibility of obtaining linear accuracy evaluation of approximate solutions to inverse problems
- On a class of finite-difference schemes for solving ill-posed Cauchy problems in Banach spaces
- Can an a priori error estimate for an approximate solution of an ill-posed problem be comparable with the error in data?
- Conditionally well-posed and generalized well-posed problems
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