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• W2017373604 abstract "A backward heat conduction problem is reduced to an operator equation Aq = ƒ and is solved in two versions by minimization of functionals R(q) = 〈Aq – 2ƒ, q〉 and J(q) = 〈Aq – ƒ, Aq – ƒ〉. The performance of the versions is compared both theoretically and numerically. An a priori convergence rate estimate has been obtained in the case of exact data and an a posteriori two-side estimate of the solution error norm has been derived in the case of noisy data. The estimates are illustrated by a model image deblurring problem. Numerical experiments demonstrated that minimization of J(q) is more effective for noisy data while R(q) works better for exact data." @default.
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• W2017373604 date "2009-01-01" @default.
• W2017373604 modified "2023-09-25" @default.
• W2017373604 title "Convergence analysis of gradient descend methods generated by two different functionals in a backward heat conduction problem" @default.
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• W2017373604 doi "https://doi.org/10.1515/jiip.2009.042" @default.
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