A stochastic procedure to solve linear ill-posed problems
| Autor: | Nadji Rahmania, Fouad Maouche, Abdelnasser Dahmani |
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| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Well-posed problem 0209 industrial biotechnology Mathematical optimization Hilbert space 020206 networking & telecommunications 02 engineering and technology Inverse problem Type (model theory) Domain (mathematical analysis) symbols.namesake 020901 industrial engineering & automation Exact solutions in general relativity Rate of convergence 0202 electrical engineering electronic engineering information engineering symbols Deconvolution Mathematics |
| Zdroj: | Communications in Statistics - Theory and Methods. 46:1519-1531 |
| ISSN: | 1532-415X 0361-0926 |
| DOI: | 10.1080/03610926.2015.1019153 |
| Popis: | In this work, we propose a stochastic procedure of Robbins–Monro type to resolve linear inverse problems in Hilbert space. We study the probability of large deviation between the exact solution and the approximated one and build a confidence domain for the approximated solution while precising the rate of convergence. To check the validity of our work, we give a simulation application into a deconvolution problem. |
| Databáze: | OpenAIRE |
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