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Nikolai A. Sidorov

Publications and source records attributed to Nikolai A. Sidorov.

3 recordsLinked to original sources

Irregular linear systems of PDEs with the conditions on solutions' projections

The theory of complete generalized Jordan sets is employed to reduce the PDE with the irreversible linear operator $B$ of finite index to the regular problems. It is demonstrated how the question of the choice of boundary conditions is connected with the $B$-Jordan structure of coefficients of PDE. The various approaches shows the combination alternative Lyapunov method, Jordan structure coefficients and skeleton decomposition of irreversible linear operator from the main part equation are among the most powerfull methods to attack such challenging problem. On this base the complex problem of the {\it correct choice of boundary conditions} for the wide class of the singular PDE can be solved. Aggregated existence and uniqueness theorems can be proved, solution may continuously depend on the function determied from the experiments. Such theory can be applied to the integral--differential equations with partial derivatives.

math.AP↗

On Perturbation Method for the First Kind Equations: Regularization and Application

One of the most common problems of scientific applications is computation of the derivative of a function specified by possibly noisy or imprecise experimental data. Application of conventional techniques for numerically calculating derivatives will amplify the noise making the result useless. We address this typical ill-posed problem by application of perturbation method to linear first kind equations $Ax=f$ with bounded operator $A.$ We assume that we know the operator $\tilde{A}$ and source function $\tilde{f}$ only such as $||\tilde{A} - A||\leq δ_1,$ $||\tilde{f}-f||< δ_2.$ The regularizing equation $\tilde{A}x + B(α)x = \tilde{f}$ possesses the unique solution. Here $α\in S,$ $S$ is assumed to be an open space in $\mathbb{R}^n,$ $0 \in \overline{S},$ $α= α(δ).$ As result of proposed theory, we suggest a novel algorithm providing accurate results even in the presence of a large amount of noise.

math.FA↗