• Title/Summary/Keyword: a priori choice rule

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ON THREE SPECTRAL REGULARIZATION METHODS FOR A BACKWARD HEAT CONDUCTION PROBLEM

  • Xiong, Xiang-Tuan;Fu, Chu-Li;Qian, Zhi
    • Journal of the Korean Mathematical Society
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    • v.44 no.6
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    • pp.1281-1290
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    • 2007
  • We introduce three spectral regularization methods for solving a backward heat conduction problem (BHCP). For the three spectral regularization methods, we give the stability error estimates with optimal order under an a-priori and an a-posteriori regularization parameter choice rule. Numerical results show that our theoretical results are effective.

REGULARIZED SOLUTION TO THE FREDHOLM INTEGRAL EQUATION OF THE FIRST KIND WITH NOISY DATA

  • Wen, Jin;Wei, Ting
    • Journal of applied mathematics & informatics
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    • v.29 no.1_2
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    • pp.23-37
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    • 2011
  • In this paper, we use a modified Tikhonov regularization method to solve the Fredholm integral equation of the first kind. Under the assumption that measured data are contaminated with deterministic errors, we give two error estimates. The convergence rates can be obtained under the suitable choices of regularization parameters and the number of measured points. Some numerical experiments show that the proposed method is effective and stable.

STABLE APPROXIMATION OF THE HEAT FLUX IN AN INVERSE HEAT CONDUCTION PROBLEM

  • Alem, Leila;Chorfi, Lahcene
    • Communications of the Korean Mathematical Society
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    • v.33 no.3
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    • pp.1025-1037
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    • 2018
  • We consider an ill-posed problem for the heat equation $u_{xx}=u_t$ in the quarter plane {x > 0, t > 0}. We propose a new method to compute the heat flux $h(t)=u_x(1,t)$ from the boundary temperature g(t) = u(1, t). The operator $g{\mapsto}h=Hg$ is unbounded in $L^2({\mathbb{R}})$, so we approximate h(t) by $h_{\delta}(t)=u_x(1+{\delta},\;t)$, ${\delta}{\rightarrow}0$. When noise is present, the data is $g_{\epsilon}$ leading to a corresponding heat $h_{{\delta},{\epsilon}}$. We obtain an estimate of the error ${\parallel}h-h_{{\delta},{\epsilon}}{\parallel}$, as well as the error when $h_{{\delta},{\epsilon}}$ is approximated by the trapezoidal rule. With an a priori choice rule ${\delta}={\delta}({\epsilon})$ and ${\tau}={\tau}({\epsilon})$, the step size of the trapezoidal rule, the main theorem gives the error of the heat flux as a function of noise level ${\epsilon}$. Numerical examples show that the proposed method is effective and stable.