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<article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns:ali="http://www.niso.org/schemas/ali/1.0/" article-type="research-article" dtd-version="1.2" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">Discrete and Continuous Models and Applied Computational Science</journal-id><journal-title-group><journal-title xml:lang="en">Discrete and Continuous Models and Applied Computational Science</journal-title><trans-title-group xml:lang="ru"><trans-title>Discrete and Continuous Models and Applied Computational Science</trans-title></trans-title-group></journal-title-group><issn publication-format="print">2658-4670</issn><issn publication-format="electronic">2658-7149</issn><publisher><publisher-name xml:lang="en">Peoples' Friendship University of Russia named after Patrice Lumumba (RUDN University)</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="publisher-id">30327</article-id><article-id pub-id-type="doi">10.22363/2658-4670-2022-30-1-62-78</article-id><article-categories><subj-group subj-group-type="toc-heading" xml:lang="en"><subject>Articles</subject></subj-group><subj-group subj-group-type="toc-heading" xml:lang="ru"><subject>Статьи</subject></subj-group><subj-group subj-group-type="article-type"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title xml:lang="en">Finite-difference methods for solving 1D Poisson problem</article-title><trans-title-group xml:lang="ru"><trans-title>Конечно-разностные методы решения 1D задачи Пуассона</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-9297-9839</contrib-id><name-alternatives><name xml:lang="en"><surname>Ndayisenga</surname><given-names>Serge</given-names></name><name xml:lang="ru"><surname>Ндайисенга</surname><given-names>С.</given-names></name></name-alternatives><bio xml:lang="en"><p>Student of Department of Applied Probability and Informatics</p></bio><email>1032195775@rudn.ru</email><xref ref-type="aff" rid="aff1"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-1856-4643</contrib-id><name-alternatives><name xml:lang="en"><surname>Sevastianov</surname><given-names>Leonid A.</given-names></name><name xml:lang="ru"><surname>Севастьянов</surname><given-names>Л. А.</given-names></name></name-alternatives><bio xml:lang="en"><p>Doctor of Physical and Mathematical Sciences, Professor of Department of Applied Probability and Informatics of Peoples’ Friendship University of Russia (RUDN University), Leading Researcher of Bogoliubov Laboratory of Theoretical Physics, JINR</p></bio><email>sevastianov-la@rudn.ru</email><xref ref-type="aff" rid="aff1"/><xref ref-type="aff" rid="aff2"/></contrib><contrib contrib-type="author"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-3645-1060</contrib-id><name-alternatives><name xml:lang="en"><surname>Lovetskiy</surname><given-names>Konstantin P.</given-names></name><name xml:lang="ru"><surname>Ловецкий</surname><given-names>К. П.</given-names></name></name-alternatives><bio xml:lang="en"><p>Candidate of Physical and Mathematical Sciences, Associate Professor of Department of Applied Probability and Informatics</p></bio><email>lovetskiy-kp@rudn.ru</email><xref ref-type="aff" rid="aff1"/></contrib></contrib-group><aff-alternatives id="aff1"><aff><institution xml:lang="en">Peoples’ Friendship University of Russia (RUDN University)</institution></aff><aff><institution xml:lang="ru">Российский университет дружбы народов</institution></aff></aff-alternatives><aff-alternatives id="aff2"><aff><institution xml:lang="en">Bogoliubov Laboratory of Theoretical Physics Joint Institute for Nuclear Research</institution></aff><aff><institution xml:lang="ru">Лаборатория теоретической физики им. Н.Н. Боголюбова Объединённый институт ядерных исследований</institution></aff></aff-alternatives><pub-date date-type="pub" iso-8601-date="2022-04-01" publication-format="electronic"><day>01</day><month>04</month><year>2022</year></pub-date><volume>30</volume><issue>1</issue><issue-title xml:lang="en">VOL 30, NO1 (2022)</issue-title><issue-title xml:lang="ru">ТОМ 30, №1 (2022)</issue-title><fpage>62</fpage><lpage>78</lpage><history><date date-type="received" iso-8601-date="2022-02-25"><day>25</day><month>02</month><year>2022</year></date></history><permissions><copyright-statement xml:lang="en">Copyright ©; 2022, Ndayisenga S., Sevastianov L.A., Lovetskiy K.P.</copyright-statement><copyright-statement xml:lang="ru">Copyright ©; 2022, Ндайисенга С., Севастьянов Л.А., Ловецкий К.П.</copyright-statement><copyright-year>2022</copyright-year><copyright-holder xml:lang="en">Ndayisenga S., Sevastianov L.A., Lovetskiy K.P.</copyright-holder><copyright-holder xml:lang="ru">Ндайисенга С., Севастьянов Л.А., Ловецкий К.П.</copyright-holder><ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/><license><ali:license_ref xmlns:ali="http://www.niso.org/schemas/ali/1.0/">http://creativecommons.org/licenses/by/4.0</ali:license_ref></license></permissions><self-uri xlink:href="https://journals.rudn.ru/miph/article/view/30327">https://journals.rudn.ru/miph/article/view/30327</self-uri><abstract xml:lang="en"><p style="text-align: justify;">The paper discusses the formulation and analysis of methods for solving the one-dimensional Poisson equation based on finite-difference approximations - an important and very useful tool for the numerical study of differential equations. In fact, this is a classical approximation method based on the expansion of the solution in a Taylor series, based on which the recent progress of theoretical and practical studies allowed increasing the accuracy, stability, and convergence of methods for solving differential equations. Some of the features of this analysis include interesting extensions to classical numerical analysis of initial and boundary value problems. In the first part, a numerical method for solving the one-dimensional Poisson equation is presented, which reduces to solving a system of linear algebraic equations (SLAE) with a banded symmetric positive definite matrix. The well-known tridiagonal matrix algorithm, also known as the Thomas algorithm, is used to solve the SLAEs. The second part presents a solution method based on an analytical representation of the exact inverse matrix of a discretized version of the Poisson equation. Expressions for inverse matrices essentially depend on the types of boundary conditions in the original setting. Variants of inverse matrices for the Poisson equation with different boundary conditions at the ends of the interval under study are presented - the Dirichlet conditions at both ends of the interval, the Dirichlet conditions at one of the ends and Neumann conditions at the other. In all three cases, the coefficients of the inverse matrices are easily found and the algorithm for solving the problem is practically reduced to multiplying the matrix by the vector of the right-hand side.</p></abstract><trans-abstract xml:lang="ru"><p style="text-align: justify;">В статье обсуждается постановка и анализ методов решения одномерного уравнения Пуассона на основе конечно-разностных аппроксимаций - важного и очень полезного инструмента численного исследования дифференциальных уравнений. По сути, это классический метод аппроксимации, основанный на разложении решения в ряд Тейлора. Развитие теоретических и практических результатов на базе этого метода в последние годы позволили повысить точность, стабильность и сходимость методов решения дифференциальных уравнений. Некоторые особенности этого анализа включают интересные расширения классического численного анализа начальных и граничных задач. В первой части излагается численный метод решения одномерного уравнения Пуассона, сводящийся к решению системы линейных алгебраических уравнений (СЛАУ) с ленточной симметричной положительно определённой матрицей. В качестве метода решения СЛАУ используется широко известный метод прогонки (метод Томаса). Во второй части представлен метод решения, основанный на аналитическом представлении точной обратной матрицы дискретизированного варианта уравнения Пуассона. Выражения для обратных матриц существенно зависят от типов граничных условий в исходной постановке. Представлены варианты обратных матриц для уравнения Пуассона с различными граничными условиями на концах исследуемого интервала - условиями Дирихле на обоих концах интервала, условиями Дирихле на одном из концов и Неймана на другом. Во всех трёх случаях коэффициенты обратных матриц легко вычисляются (выписываются) и алгоритм решения задачи практически сводится к умножению матрицы на вектор правой части.</p></trans-abstract><kwd-group xml:lang="en"><kwd>1D Poisson equation</kwd><kwd>finite difference method</kwd><kwd>tridiagonal matrix inversion</kwd><kwd>Thomas algorithm</kwd><kwd>Gaussian elimination</kwd></kwd-group><kwd-group xml:lang="ru"><kwd>1D уравнение Пуассона</kwd><kwd>метод конечных разностей</kwd><kwd>обращение трехдиагональной матрицы</kwd><kwd>алгоритм Томаса</kwd><kwd>исключение Гаусса</kwd></kwd-group><funding-group/></article-meta></front><body></body><back><ref-list><ref id="B1"><label>1.</label><mixed-citation>A. N. Tikhonov and A. A. 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