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In this paper, the following inverse heat conduction problem: ut = uxx, x≥0, t≥0, u(x,0) = 0, x≥0 u(1,t) = g(t), t≥0, u&z.sfnc;x→∞ is bounded, is considered again. This problem is severely ill-posed: its solution (if it exists) does not depend continuously on the data; a small perturbation in the data may cause a dramatically large error in the solution for 0 < x < 1. In this paper, a new wavelet regularization method for this problem is given. Moreover, we can easily find the regularization parameter J such that some sharp stable estimates between the exact solution and the approximate one in Hr(R)-norm meaning is given. [Copyright &y& Elsevier]

Different approaches to statistical sensitivity analysis for optimal solutions of stochastic programs are discussed and compared. Possibilities of drawing conclusions about asymptotic behavior of estimated optimal solutions by means of stability properties of auxiliary randomly perturbed convex quadratic programs are indicated and illustrated on a numerical example. [ABSTRACT FROM AUTHOR]