Shahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201A hybrid method of successive linearization method (SLM) and collocation method to steady regime of the reaction-diffusion equation1710261710.52547/CMCMA.1.2.1ENElyas ShivanianDepartment of Applied Mathematics, Imam Khomeini International University, Qazvin 34148-96818, IranEghbal MohammadiDepartment of Applied Mathematics, Imam Khomeini International University, Qazvin 34148-96818, IranJournal Article20220702This article presents a method based on combination of successive linearization method (SLM) and pseudo-spectral collocation method and then is applied on a nonlinear model of coupled diffusion and chemical reaction in a spherical catalyst pellet. It is obtained that this method can be used for nonlinear boundary value problems without difficulty because the nonlinear part of the equation becomes inactive by SLM and more, to treat the linear equation, even in the case of complicatedness, is straightforward by pseudo-spectral collocation method. Also, the results reveal the high efficiency with reliable accuracy of this hybrid method.https://cmcma.sbu.ac.ir/article_102617_795722081c466785a1994b78c2b2a6f6.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201An efficient iterative method for finding the Moore-Penrose and Drazin inverse of a matrix81910276710.52547/CMCMA.1.2.8ENRaziyeh ErfanifarFaculty of Mathematics and Statistics, Malayer University, Malayer, Iran.Journal Article20220728In this paper, a third order convergent method for finding the Moore-Penrose inverse of a matrix is presented and analysed. Then, we develop the method to find Drazin inversion. This method is very robust to find the Moore-Penrose and Drazin inverse of a matrix. Finally, numerical examples show that the efficiency of the proposed method is superior over other proposed methods.https://cmcma.sbu.ac.ir/article_102767_5fc76e94dfd1398bf9e62e3cf78d08d5.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201A class of efficient derivative free iterative method with and without memory for solving nonlinear equations202610277110.52547/CMCMA.1.2.20ENRaziyeh ErfanifarFaculty of Mathematics and Statistics, Malayer University, Malayer, Iran.Journal Article20220819In the present paper, at first, we propose a new two-step iterative method for solving nonlinear equations. This scheme is based on the Steffensen's method, in which the order of convergence is four. This iterative method requires only three functions evaluation in each iteration, therefore it is optimal in the sense of the Kung and Traub conjecture. Then we extend it to the method with memory, which the order of convergence is six. Finally, numerical examples indicate that the<br />obtained methods in terms of accuracy and computational cost are superior to the<br />famous forth-order methods.https://cmcma.sbu.ac.ir/article_102771_0c6fc87358b555518d78b49822fda387.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201Reproducing kernel method for Abel's second kind singular integral equations273410291910.52547/CMCMA.1.2.27ENNazi AbdollahiCivil Engineering Department, Miaad University, Mahabad, 59141-673635, IranSaeid AbbasbandyImam Khomeini Int. University, Qazvin, Iran.0000-0003-3385-4152Journal Article20221018Singular integral equations (SIEs) are often encountered in certain contact and fracture problems in solid mechanics. In this paper, we apply the reproducing kernel method (RKM) to give the approximate solution of Abel's second-kind singular integral equations. For solving this problem, difficulties lie in its singular term. In order to remove the singular term of the equation, an equivalent transformation is made. Solution representations are obtained in reproducing kernel Hilbert space. Numerical experiments show that our reproducing kernel method is efficient. To show the high accuracy of the method the results are compared to other numerical methods and satisfactory agreements are achieved.https://cmcma.sbu.ac.ir/article_102919_2e17673a0f76e281b2d3998b2145ed4d.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201Newton-Krylov generalized minimal residual algorithm in solving the nonlinear two-dimensional integral equations of the second kind on non-rectangular domains with an error estimate354510302010.52547/CMCMA.1.2.35ENHafez YariDepartment of Computer Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran.Mehdi DelkhoshDepartment of Mathematics and Computer Science,
Bardaskan Branch, Islamic Azad University,
Bardaskan, Iran.0000-0001-6632-4743Journal Article20221031In this paper, an applicable numerical approximation has been proposed for solving nonlinear two-dimensional integral equations (2DIEs) of the second kind on non-rectangular domains. Because directly applying the collocation methods on non-rectangular domains is difficult, in this work, at first, the integral equation is converted to an equal integral equation on a rectangular domain, then the solution is approximated by applying 2D Jacobi collocation method, the implementation of these instructions reduces the integral equation to a system of nonlinear algebraic equations, therefore, solving this system has an important role to approximate the solution. In this paper, Newton-Krylov generalized minimal residual (NK-GMRes) algorithm is used for solving the system of nonlinear algebraic equations. Furthermore, an error estimate for the presented method is investigated and several examples confirm the accuracy and efficiency of the proposed instructions.https://cmcma.sbu.ac.ir/article_103020_b11f4ccd892fbf09f23f8c31b65638f6.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201Numerical investigation of differential biological models via Gaussian RBF collocation method with genetic strategy466410307810.52547/CMCMA.1.2.46ENFardin SalehiDepartment of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, IranSoleiman Hashemi ShahrakiDepartment of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran0000-0002-6179-2254Mohammad Kazem Fallah‎Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of KoreaMohammad HemamiDepartment of Cognitive Modelling, Institute for Cognitive and Brain Sciences, Shahid Beheshti
University0000-0001-5548-0281Journal Article20221113In this paper, we use radial basis function collocation method for solving the system of differential equations in the area of biology. One of the challenges in RBF method is picking out an optimal value for shape parameter in Radial basis function to achieve the best result of the method because there are not any available analytical approaches for obtaining optimal shape parameter. For this reason, we design a genetic algorithm to detect a close optimal shape parameter. The population convergence figures, the residuals of the equations and the examination of the ASN2R and ARE measures all show the accurate selection of the shape parameter by the proposed genetic algorithm. Then, the experimental results show that this strategy is efficient in the systems of differential models in biology such as HIV and Influenza. Furthermore, we show that using our pseudo-combination formula for crossover in genetic strategy leads to convergence in the nearly best selection of shape parameter.https://cmcma.sbu.ac.ir/article_103078_d38ef33604ce1a57f37856afdaeb42bb.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201A computational method to solve fractional-order Fokker-Planck equations based on Touchard polynomials657310319110.52547/CMCMA.1.2.65ENSedigheh SabermahaniDepartment of Mathematics, Faculty of Mathematical Sciences, Alzahra University, Tehran, Iran0000-0002-7320-8908Yadollah OrdokhaniDepartment of Mathematics, Faculty of Mathematical Sciences, Alzahra University, Tehran, IranJournal Article20221017This manuscript presents a new approximation method for fractional-order Fokker-Planck equations based on Touchard polynomial approximation. We provide new Caputo and extra Caputo pseudo-operational matrices for these polynomials. Then, utilizing mentioned pseudo-operational matrices and an optimal method, the considered equation leads to a system of algebraic equations which can be solved by mathematical software. Finally, we illustrate the advantages of the suggested technique through several numerical examples.https://cmcma.sbu.ac.ir/article_103191_a65a15a361eef420f4621f235599453b.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201A new approach for solving nonlinear equations748510359110.52547/CMCMA.1.2.74ENMarzieh Dehghani-MadisehDepartment of Mathematics, Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, Ahvaz, IranJournal Article20220717In this paper, we describe and analyze an efficient method to find the roots of a general one variable function $f:\mathbb{R}\rightarrow \mathbb{R}$. The proposed method is based on partitioning an interval (that probably contains root(s) of $f$) into subintervals. From this point of view, we name this method a finite element approach for root finding. Also the convergence analysis of the presented method is presented. The new approach can be generalized to estimate the roots of the multivariable functions in higher dimensions. Also it is capable to find all of the roots of the function on a determined interval. Finally, numerical examples are given to illustrate the effectiveness of the new method.https://cmcma.sbu.ac.ir/article_103591_563a220836590ebf8c4fbd8cc07f065f.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201On two convex variational models and their iterative solutions for selective segmentation of images with intensity inhomogeneity8610310359210.52547/CMCMA.1.2.86ENLiam BurrowsCentre for Mathematical Imaging Techniques and Department of Mathematical Sciences, University of Liverpool, Liverpool L19 7ZL, United Kingdom.Ke ChenDepartment of Mathematical Sciences, University of Liverpool, Liverpool, UKFrancesco TorellaLiverpool Vascular & Endovascular Service, Royal Liverpool and Broadgreen University Hospitals NHS Trust, Liverpool, L7 8XP, United KingdomJournal Article20230313Treating images as functions and using variational calculus,<br />mathematical imaging offers to design novel and continuous methods, outperforming traditional methods based on matrices, for modelling real life tasks in image processing.<br />Image segmentation is one of such fundamental tasks as many application areas demand a reliable segmentation method. Developing reliable selective segmentation algorithms is<br />particularly important in relation to training data preparation in modern machine learning as accurately isolating a specific object in an image with minimal user input is a valuable tool. When an image's intensity is consisted of mainly piecewise constants, convex models are available.<br />Different from previous works, this paper<br />proposes two convex models that are capable of segmenting local features defined by geometric constraints for images having intensity inhomogeneity.<br />Our new, local, selective and convex variants are extended from the non-convex Mumford-Shah model intended for global segmentation.<br />They have fundamentally improved on previous selective models that assume intensity of piecewise constants. Comparisons with related models are conducted to illustrate the advantages of our new models.https://cmcma.sbu.ac.ir/article_103592_8ef660fd9eacf6128182886e30b24cec.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201Numerical solution for solving magnetohydrodynamic (MHD) flow of nanofluid by least squares support vector regression10412110363010.52547/CMCMA.1.2.104ENAida PakniyatDepartment of Computer and Data Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, IranJournal Article20230510This paper introduces a new numerical solution based on the least squares support vector machine (LS-SVR) for solving nonlinear ordinary differential equations of high dimensionality. We apply the quasilinearization method to linearize the magnetohydrodynamic (MHD) flow of nanofluid around a stretching cylinder, thereby transforming it into a linear problem. We then utilize LS-SVR with fractional Hermite functions as basis functions to solve this problem over a semi-infinite interval. Our numerical results confirm the effectiveness of this approach.https://cmcma.sbu.ac.ir/article_103630_82223eb2e1c99c9ffd046d302c15d050.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201On modified decomposition of interval matrices and its application12212810363110.52547/CMCMA.1.2.122ENMarzieh Dehghani-MadisehDepartment of Mathematics, Faculty of Mathematical Sciences and Computer, Shahid Chamran University of Ahvaz, Ahvaz, IranJournal Article20220721In this paper we present the LU decomposition of a generalized interval matrix ${\bf{A}}$ under a modified interval arithmetic. This modified interval arithmetic is defined on generalized intervals and possesses group properties with respect to the addition and multiplication operations. These properties cause that the two computed generalized interval matrices ${\bf{L}}$ and ${\bf{U}}$ from the LU decomposition satisfy ${\bf{A}}={\bf{L}}{\bf{U}}$, with equality in modified interval arithmetic instead of the weaker inclusion in the classical interval arithmetic. Some applications of the new technique for solving interval linear systems are given and effectiveness of the new approach is investigated along some numerical tests.https://cmcma.sbu.ac.ir/article_103631_02e997c49a38be87dd0c5baaf35f119e.pdfShahid Beheshti UniversityComputational Mathematics and Computer Modeling with Applications (CMCMA)2783-48591220221201A hybrid numerical method based on the generalized pseudospectral method for solving nonlinear differential equations12913810374110.52547/CMCMA.1.2.12ENMehdi DelkhoshDepartment of Mathematics and Computer Science,
Bardaskan Branch, Islamic Azad University,
Bardaskan, Iran.0000-0001-6632-4743Reza Arefi ShirvanDepartment of Biomedical Engineering, Bardaskan Branch, Islamic Azad University, Bardaskan, Iran.Journal Article20230603In this paper, a hybrid numerical method using generalized pseudospectral and Newton-Kantorovich quasilinearization methods is presented to solve nonlinear differential equations. Initially, generalized Lagrange functions as basic functions are introduced and then derivative operational matrices for these functions are presented. Then using these new functions, the generalized pseudospectral method is constructed as a numerical method. Finally, this method and the Newton-Kantorovich quasilinearization method are combined to produce an efficient method. Because of the use of derivative operating matrices and the conversion of any nonlinear differential equation into sequences of linear differential equations, the implementation of this method does not require mathematically to calculate the derivative and the computational costs are also reduced. To illustrate the efficiency, accuracy, and convergence of the method, the proposed method is implemented on two famous equations and the results are compared with other methods.https://cmcma.sbu.ac.ir/article_103741_adde068141b49bc8bbcb1635692827f2.pdf