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Numerical solution of nonlinear [formula omitted]-dimensional Fredholm integral equations using iterative Newton–Cotes rules J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-16 Hamid Mottaghi Golshan
In this work, we propose a numerical iterative method to solve multidimensional integral equations based on Picard iteration and Newton–Cotes rules in a cubic domain. We show that the combination of successive approximation (Picard) sequence and Newton–Cotes rules provides an approximate solution to the multidimensional Fredholm–Urysohn integral equation of the second kind. Convergence analysis and
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A modified weak Galerkin finite element method for the Maxwell equations on polyhedral meshes J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-15 Chunmei Wang, Xiu Ye, Shangyou Zhang
We introduce a new numerical method for solving time-harmonic Maxwell’s equations via the modified weak Galerkin technique. The inter-element functions of the weak Galerkin finite elements are replaced by the average of the two discontinuous polynomial functions on the two sides of the polygon, in the modified weak Galerkin (MWG) finite element method. With the dependent inter-element functions, the
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Higher-order KKT optimality conditions through contingent derivatives for constrained nonsmooth vector equilibrium problems J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-13 Tran Van Su, Dinh Dieu Hang
In this paper, we deal with some higher-order optimality conditions for local strict efficient solutions to a nonsmooth vector equilibrium problem with set, cone and equality constraints. For this aim, the concept of stable and steady functions ( and integer) for single-valued functions and some constraint qualifications of higher order in terms of contingent derivatives are proposed accordingly. We
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Stabilized BB projection algorithm for large-scale convex constrained nonlinear monotone equations to signal and image processing problems J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-12 Jiayun Rao, Chaozhi Yu, Na Huang
The Barzilai–Borwein (BB) method is a popular and efficient gradient method for solving large-scale unconstrained optimization problems. In general, it converges much faster than the steepest descent (Cauchy) method. However, it may not converge, even when the objective function is strongly convex. To overcome this, by virtue of bounding the distance between sequential iterates, [Burdakov et al. (2019)]
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Linearly-fitted energy-mass-preserving schemes for Korteweg–de Vries equations J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-10 Kai Liu, Ting Fu
In this paper, second-order and fourth-order linearly-fitted energy-mass-preserving schemes for Korteweg–de Vries (KdV) equations are proposed. First, the KdV equation is semi-discretized into an oscillatory Hamiltonian system of Ordinary Differential Equations (ODEs) by semi-discretizations of the skew adjoint operator and the Hamiltonian in the Hamiltonian form of the KdV equation. Then the resulting
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Symbolic matrix factorization for differential-algebraic equations index reduction J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-08 Davide Stocco, Enrico Bertolazzi
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The two-point Padé approximation problem and its Hankel vector J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-07 Bohui Ban, Xuzhou Zhan, Yongjian Hu
The two-point Padé approximation problem is to find a ratio of two coprime polynomials with some constraints on their degrees to approximate a function whose power series expansions at the origin and at infinity are given. In this paper, we introduce the Hankel vector for the two-point Padé approximation problem and establish the intrinsic connections between the two-point Padé approximation problem
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New zeroing NN models with nonconvex saturated activation functions in noisy environments for quadratic minimization dynamics and control J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-07 Tangtao Luo, Guancheng Wang, Xiuchun Xiao
Fast and accurate solutions are of great importance to dynamic quadratic minimization (DQM) problems in many fields of engineering and science. By making a general survey of existing methods, DQM problems are capable of being solved very validly while there is still a lack of a superior performance method to face a noisy environment. To tolerate the ubiquitous noise, a zeroing neural network with a
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Numerical solution to a Parabolic-ODE Solow model with spatial diffusion and technology-induced motility J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-06 N. Ureña, A.M. Vargas
This work studies a parabolic-ODE PDE’s system which describes the evolution of the physical capital “” and technological progress “”, using a meshless method in one and two dimensional bounded domain with regular boundary. The well-known Solow model is extended by considering the spatial diffusion of both capital and technology. Moreover, we study the case in which no spatial diffusion of the technology
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Network traffic recovery from link-load measurements using tensor triple decomposition strategy for third-order traffic tensors J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-06 Zhenyu Ming, Zhenzhi Qin, Liping Zhang, Yanwei Xu, Liqun Qi
Network traffic data is the pivot of input in many network tasks but its direct measurement can be insufferably costly. In this paper, we propose a network traffic recovery method which only requires the conveniently measurable link-load traffics. We arrange the traffic data as a third-order tensor and utilize the triple decomposition technique proposed very recently by Qi et al. (2021). The studied
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Corrigendum to "A pressure-robust numerical scheme for the Stokes equations based on the WOPSIP DG approach'' [Journal of Computational and Applied Mathematics 445 (2024) 115819] J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-05 Yuping Zeng, Liuqiang Zhong, Feng Wang, Shangyou Zhang, Mingchao Cai
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Towards computing the harmonic-measure distribution function for the middle-thirds Cantor set J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-04 Christopher C. Green, Mohamed M.S. Nasser
This paper is concerned with the numerical computation of the harmonic-measure distribution function, or -function for short, associated with a particular planar domain. This function describes the hitting probability of a Brownian walker released from some point with the boundary of the domain. We use a fast and accurate boundary integral method for the numerical calculation of the -functions for
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Numerical method for singular drift stochastic differential equation driven by fractional Brownian motion J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-04 Hao Zhou, Yaozhong Hu, Jingjun Zhao
In this paper, we study the stochastic differential equation with singular drift coefficient driven by fractional Brownian motion with Hurst parameter . We obtain that the optimal convergence rate of the backward Euler method is 1.0. The constant elasticity of variance model and the Aït-Sahalia interest rate model are performed as numerical experiments to validate our theoretical results.
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Real option pricing under the regime-switching model with jumps on a finite time horizon J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-03 Sunju Lee, Younhee Lee
We consider an irreversible investment decision problem on a finite time horizon where an instantaneous cash flow process of a firm follows a regime-switching jump-diffusion (RSJD) model. The value of a project can be derived from a partial integro-differential equation (PIDE) and then we obtain a closed-form solution of the PIDE. It is proved that the value of the project converges to the solution
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A one-dimensional branching rule based branch-and-bound algorithm for minimax linear fractional programming J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-03 Peiping Shen, Yaping Deng, Yafei Wang
This paper investigates a type of minimax linear fractional program (MLFP) that often occurs in practical problems such as design of electronic circuits, finance and investment. We first transform the MLFP problem into an equivalent problem (EP) by using the Charnes–Cooper transformation and introducing an auxiliary variable. A linear relaxation strategy should simplify the nonconvex parts of the constraints
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Uncertain stochastic hybrid zero-sum games based on forward uncertain difference equations and backward stochastic difference equations J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-03 Xin Chen, Ziqiang Lu, Dongmei Yuan, Yu Shao
We investigate the interplay between forward uncertain difference equations and backward stochastic difference equations, which belong to distinct mathematical frameworks: uncertainty theory and probability theory, respectively. By establishing the connections between uncertain expectation, probabilistic expectation, and chance expectation, we develop the chance theory framework for analyzing uncertain
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A recursive method for fractional Hawkes intensities and the potential approach of credit risk J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-02 John-John Ketelbuters, Donatien Hainaut
This article explores the potential approach for credit risk, which is an alternative to structural and reduced models. In the context of credit risk, it consists in assuming that the survival probability of a company is equal to the ratio of the expected value of a supermartingale divided by its initial value. This approach, that was previously used for modeling the term structure of interest rates
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Multinode Shepard method for two-dimensional elliptic boundary problems on different shaped domains J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-02 Francesco Dell’Accio, Filomena Di Tommaso, Elisa Francomano
In this paper, we continue the study on the application of multinode Shepard method to numerically solve elliptic Partial Differential Equations (PDEs) equipped with various conditions at the boundary of domains of different shapes. In particular, for the first time, the multinode Shepard method is proposed to solve elliptic PDEs with Dirichlet and/or Neumann boundary conditions. The method has been
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Exact perturbation approximations for the conditional moments of a multifactor CIR term structure model with a weak mean-reversion influence J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-01 Cheng-Hsun Wu
In this paper, we derive exact series expressions for the conditional moments of a multivariate Cox-Ingersoll-Ross term structure model with a weak mean-reversion effect assumption. First, we construct the perturbation solution for the system of Riccati equations, which illustrates the conditional characteristic function of this term structure model. Second, we derive the power series expression for
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A Bisection method for computing the proximal operator of the [formula omitted]-norm for any [formula omitted] with application to Schatten [formula omitted]-norms J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-04-01 Yulan Liu, Rongrong Lin
The computation of the proximal operator of the -norm is critically important for non-convex optimization with the sparse-promoting -regularization. Over the last decade, many exact and inexact numerical methods for simple and efficient computation of the proximal operator have been proposed in the literature. For this purpose, a Bisection method is given in the paper, which is based on the properties
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Calibration of European option pricing model in uncertain environment: Valuation of uncertainty implied volatility J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-26 Jinwu Gao, Ruru Jia, Idin Noorani, Farshid Mehrdoust
Uncertain differential equations have been widely used in modeling financial markets, and option pricing formulae have been obtained by employing these equations. However, according to the existing literature, the parameter estimation of the option pricing model driven by the uncertain differential equation has not been evaluated so far, and the parameters have been assumed to be arbitrary numbers
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Numerical approximation of Fredholm integral equation by the constrained mock-Chebyshev least squares operator J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-26 Francesco Dell’Accio, Domenico Mezzanotte, Federico Nudo, Donatella Occorsio
In this paper, we propose two numerical approaches for approximating the solution of the following kind of integral equation where is the unknown solution, , are given functions not necessarily known in the analytical form, and is a Jacobi weight. The proposed projection methods are based on the constrained mock-Chebyshev least squares polynomials, and starting from data known at equally spaced points
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Interpolation by polygon rolling motions for approximate sweep computation J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-25 Jana Vráblíková, Vanessa Ortler, Bert Jüttler, Zbyněk Šír
We interpolate planar rigid body motions by piecewise rotational and translational motions. The trajectories of all points are then arc splines, i.e., curves composed of circular arcs or line segments. For objects with arc spline boundaries, the boundary of the volume swept by the object and the motion is then again an arc spline. We prove that the distance between the original motion and its approximation
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Fast ellipsoidal conformal and quasi-conformal parameterization of genus-0 closed surfaces J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-21 Gary P.T. Choi
Surface parameterization plays a fundamental role in many science and engineering problems. In particular, as genus-0 closed surfaces are topologically equivalent to a sphere, many spherical parameterization methods have been developed over the past few decades. However, in practice, mapping a genus-0 closed surface onto a sphere may result in a large distortion due to their geometric difference. In
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An accelerated relaxed-inertial strategy based CGP algorithm with restart technique for constrained nonlinear pseudo-monotone equations to image de-blurring problems J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-21 Xianzhen Jiang, Zefeng Huang
In this paper, an accelerated spectral conjugate gradient projection algorithm is proposed for solving the constrained nonlinear pseudo-monotone equations. We set a restart procedure related to the conjugate parameter in the search direction of the spectral conjugate gradient method. We use an accelerated gradient descent scheme to generate the spectral parameter. Finally, we adopt the relaxed-inertial
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A neural network based model for multi-dimensional non-linear Hawkes processes J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-20 Sobin Joseph, Shashi Jain
This paper introduces the Neural Network for Non-linear Hawkes processes (NNNH), a non-parametric method based on neural networks to fit non-linear Hawkes processes. Our method is suitable for analysing large datasets in which events exhibit both mutually-exciting and inhibitive patterns. The NNNH approach models the individual kernels and the base intensity of the non-linear Hawkes process using feed
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A Banach spaces-based fully mixed virtual element method for the stationary two-dimensional Boussinesq equations J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-19 Gabriel N. Gatica, Zeinab Gharibi
In this paper we extend recent results obtained for the Navier–Stokes equations to propose and analyze a new fully mixed virtual element method (mixed-VEM) for the stationary two-dimensional Boussinesq equations appearing in non-isothermal flow phenomena. The model consists of a Navier–Stokes type system, modeling the velocity and the pressure of the fluid, coupled to an advection-diffusion equation
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Learning scattering waves via coupling physics-informed neural networks and their convergence analysis J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-16 Rui Zhang, Yu Gao
Time harmonic acoustic wave scattering problems modeled by the unbounded Helmholtz equations are widely used in medical and military fields. A fast and flexible solver for the Helmholtz equation is expected in engineering applications. Fortunately, data-driven neural network algorithms show extraordinary promise in scientific computing and artificial intelligence. We solve the Helmholtz equation by
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A hybrid BFGS-Like method for monotone operator equations with applications J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-16 A.B. Abubakar, P. Kumam, H. Mohammad, A.H. Ibrahim, T. Seangwattana, B.A. Hassan
In this paper, a hybrid three-term conjugate gradient (CG) method is proposed to solve constrained nonlinear monotone operator equations. The search direction is computed such that it is close to the direction obtained by the memoryless Broyden–Fletcher–Goldferb–Shanno (BFGS) method. Without any condition, the search direction is sufficiently descent and bounded. Moreover, based on some conditions
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Efficient least squares approximation and collocation methods using radial basis functions J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-15 Yiqing Zhou, Daan Huybrechs
We describe an efficient method for the approximation of functions using radial basis functions (RBFs), and extend this to a solver for boundary value problems on irregular domains. The method is based on RBFs with centers on a regular grid defined on a bounding box, with some of the centers outside the computational domain. The equation is discretized using collocation with oversampling, with collocation
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An explicit–implicit Generalized Finite Difference scheme for a parabolic–elliptic density-suppressed motility system J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-15 Federico Herrero-Hervás
In this work, a Generalized Finite Difference (GFD) scheme is presented for effectively computing the numerical solution of a parabolic–elliptic system modeling a bacterial strain with density-suppressed motility. The GFD method is a meshless method known for its simplicity for solving non-linear boundary value problems over irregular geometries. The paper first introduces the basic elements of the
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Calibration estimation of distribution function based on multidimensional scaling of auxiliary information J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-14 Sergio Martínez, María D. Illescas, María del Mar Rueda
The distribution function is a functional parameter of great interest in many research areas, such as medicine or economics. Among other properties, it facilitates the estimation of parameters such as quantiles. Accordingly, techniques are needed to estimate this function efficiently. Survey statisticians have access to large, high-dimension databases and use them to optimise the estimates obtained
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A novel directly energy-preserving method for charged particle dynamics J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-13 Yexin Li, Ping Jiang, Haochen Li
In this paper, we apply the coordinate increment discrete gradient (CIDG) method to solve the Lorentz force system which can be written as a non-canonical Hamiltonian system. Then we can obtain a new energy-preserving CIDG-I method for the system. The CIDG-I method can combine with its adjoint method CIDG-II which is also a energy-preserving method to form a new method, namely CIDG-C method. The CIDG-C
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An efficient inertial subspace minimization CG algorithm with convergence rate analysis for constrained nonlinear monotone equations J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-11 Taiyong Song, Zexian Liu
Conjugate gradient (CG) methods are efficient algorithms for unconstrained optimization. An inertial step strategy is usually used to accelerate iterative methods. In 1995, Yuan and Stoer (1995) introduced a subspace study on CG algorithms and presented some subspace minimization CG (SMCG) methods. Motivated by these approaches, we incorporate a new inertial step strategy in SMCG methods and present
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Lattice factorization based causal symmetric paraunitary matrix extension and construction of symmetric orthogonal multiwavelets J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-11 ChiWon Ri
In this paper, we propose a lattice factorization based symmetric paraunitary matrix extension method to design a causal symmetric paraunitary multifilter banks(PUMFBs) and construct compactly supported symmetric orthogonal multiwavelets by using the method.
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Small area estimation with partially linear mixed-t model with measurement error J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-11 Seyede Elahe Hosseini, Davood Shahsavani, Mohammad Reza Rabiei, Mohammad Arashi
In small area estimation (SAE), using direct conventional methods will not lead to reliable estimates because the sample size is small compared to the population. Small Area Estimation under Fay Herriot Model is used to borrow strength from auxiliary variables to improve the effectiveness of a sample size. However, the normality assumption is a limiting assumption for heavy-tailed data and outlying
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Numerical analysis of light-controlled drug delivery systems J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-07 J.A. Ferreira, H.P. Gómez, L. Pinto
In this paper, we solve a non-linear reaction–diffusion system with Dirichlet–Neumann mixed boundary conditions using a finite difference method (FDM) in space and the implicit midpoint method in time. This type of system appears, e.g., in the mathematical modeling of light-controlled drug delivery. One of the key results of this paper is the proof that the method has superconvergence second-order
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A family of two-step second order Runge–Kutta–Chebyshev methods J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-07 Andrew Moisa
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An efficient numerical scheme to solve generalized Abel’s integral equations with delay arguments utilizing locally supported RBFs J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-06 Alireza Hosseinian, Pouria Assari, Mehdi Dehghan
Hereditary effects are commonly observed in diverse scientific domains such as engineering, economics, biology, mathematics, and physics. In the model of atomic irradiation of solids with unbounded cross-sectional areas, determining the average number of atoms displaced has been achieved through delay systems that incorporate the consideration of past states. In this study, we employ the discrete collocation
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On approximation for time-fractional stochastic diffusion equations on the unit sphere J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-06 Tareq Alodat, Quoc T. Le Gia, Ian H. Sloan
This paper develops a two-stage stochastic model to investigate the evolution of random fields on the unit sphere in . The model is defined by a time-fractional stochastic diffusion equation on governed by a diffusion operator with a time-fractional derivative defined in the Riemann–Liouville sense. In the first stage, the model is characterized by a homogeneous problem with an isotropic Gaussian random
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Midpoint splitting methods for nonlinear space fractional diffusion equations J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-06 Hongliang Liu, Tan Tan
We propose a midpoint splitting method to solve nonlinear space fractional diffusion equations. We first discretize the equation in space using the weighted and shifted Grünwald difference (WSGD) operators. Then, we obtain the fully discrete scheme of the equation using the midpoint splitting method, which can alleviate computational costs and mitigate instability issues that may arise. We establish
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Pricing longevity bond with affine-jump-diffusion multi-cohort mortality model J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-06 Jingtong Xu, Xu Chen, Yuying Yang
Facing increasingly severe longevity problems, traditional longevity risk management methods are no longer the final answer. Longevity risk securitization provides a good hedging method, which transfers the longevity risks to a wider capital market and realizes the cross-market transfer of risks. The pricing process of longevity derivatives depends on the underlying risk factors (stochastic processes
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FDM/FEM for nonlinear convection–diffusion–reaction equations with Neumann boundary conditions—Convergence analysis for smooth and nonsmooth solutions J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-05 J.A. Ferreira, G. Pena
This paper aims to present in a systematic form the stability and convergence analysis of a numerical method defined in nonuniform grids for nonlinear elliptic and parabolic convection–diffusion–reaction equations with Neumann boundary conditions. The method proposed can be seen simultaneously as a finite difference scheme and as a fully discrete piecewise linear finite element method. We establish
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An uncertainty theory based tri-objective behavioral portfolio selection model with loss aversion and reference level using a modified evolutionary root system growth algorithm J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-05 Yuefen Chen, Bo Li
The outbreak of uncertain events, e.g., financial crisis, regional conflict and abrupt contagion, has a significant impact on residents’ income. Hence, the wealth management and portfolio selection become more and more important. In addition, the behavioral finance believes that decision-making process of the investors not only depend on utility maximization, but also on who to compare with. It differs
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Some stabilities of stochastic differential equations with delay in the G-framework and Euler–Maruyama method J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-05 Haiyan Yuan, Quanxin Zhu
This paper discusses some stabilities of stochastic differential equations with delay in the G-framework (G-SDDEs, in short) and Euler-Maruyama method. We construct a weaker condition instead of using the Lyapunov functional method to obtain the -th moment exponential stability of the G-SDDE. We prove that the Euler-Maruyama method can reproduce the -th moment exponential stability of the G-SDDE under
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A sublinear functional based approximated equivalence to optimality and duality for multiobjective programming in complex spaces J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-04 Nisha Pokharna, Indira P. Tripathi
In this paper, we introduce a new approximation approach for a class of multiobjective programming problems in complex spaces and their duals. Using a sublinear functional, an approximated problem is constructed at a given feasible solution of the original problem. The equivalence between the solution of the considered multiobjective complex programming problem and its approximated problem is established
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A two-level finite element method with grad-div stabilizations for the incompressible Navier–Stokes equations J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-04 Yueqiang Shang
This article presents and studies a two-level grad-div stabilized finite element discretization method for solving numerically the steady incompressible Navier–Stokes equations. The method consists of two steps. In the first step, we compute a rough solution by solving a nonlinear Navier–Stokes system on a coarse grid. And then, in the second step, we pass the coarse grid solution to a fine grid to
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Unconditional error analysis of linearized BDF2 mixed virtual element method for semilinear parabolic problems on polygonal meshes J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-04 Wanxiang Liu, Yanping Chen, Jianwei Zhou, Qin Liang
In this paper, we construct, analyze, and numerically validate a class of -mixed virtual element method for the semilinear parabolic problem in mixed form, in which the parabolic problem is reformulated in terms of the velocity and the pressure of the time-dependent Darcy flow. The Newton linearized method for the nonlinear term is designed to cooperate with the second-order backward differentiation
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A numerical solution of singularly perturbed Fredholm integro-differential equation with discontinuous source term J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-03-01 Ajay Singh Rathore, Vembu Shanthi
In this paper, we investigate a singularly perturbed Fredholm integro-differential problem with a discontinuous source term, leading to the formation of interior layers in the solution at the point of discontinuity. We apply the exponentially fitted mesh method to solve the problem. Our analysis demonstrates that the method exhibits almost first-order convergence in the maximum norm, independent of
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Performance enhancement through portfolio optimization of delayed insider information: An analysis and implementation study J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-28 Sandra Ranilla-Cortina, Jesús Vigo-Aguiar
This paper addresses the classical problem of in a financial market where an with privileged information exists, along with a in the information flow. The paper calculates the wealth evolution process and determines the optimal portfolio that maximizes the expected final wealth for various well-known financial models, including Black–Scholes-Merton, Heston, Vasicek, Hull–White, and Cox-Ingersoll-Ross
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A monolithic space–time temporal multirate finite element framework for interface and volume coupled problems J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-28 Julian Roth, Martyna Soszyńska, Thomas Richter, Thomas Wick
In this work, we propose and computationally investigate a monolithic space–time multirate scheme for coupled problems. The novelty lies in the monolithic formulation of the multirate approach as this requires a careful design of the functional framework, corresponding discretization, and implementation. Our method of choice is a tensor-product Galerkin space–time discretization. The developments are
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On the compound Poisson phase-type process and its application in shock models J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-28 Dheeraj Goyal, Min Xie
In this paper, the compound Poisson phase-type process is defined and analyzed. This paper proves that for a non-negative compound Poisson phase-type process, the compound value for all the arrivals by a given time can be approximated by a phase-type distribution. As an application of this process, three different shock models are studied: the cumulative shock model, a degradation-threshold-shock model
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Construction of Bézier surfaces with minimal quadratic energy for given diagonal curves J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-27 Yong-Xia Hao, Wen-Qing Fei
Diagonal curve is one of the most important shape measurements of tensor-product Bézier surfaces. An approach to construct Bézier surfaces with energy-minimizing from two prescribed diagonal curves is presented in this paper. Firstly, a general second order functional energy is formulated with several parameters. This functional includes many common functionals as special cases, such as the Dirichlet
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A color image fusion model by saturation-value total variation J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-27 Wei Wang, Yuming Yang
In this paper, we propose and develop a novel color image fusion model by using saturation-value total variation. In the proposed model, we develop a variational approach containing an energy functional to determine the weighting mask functions and the fused image together. The objective fused image is modeled by using the saturation-value total variation regularization. The data-fitting term is formulated
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Dynamical analysis and explicit traveling wave solutions to the higher-dimensional generalized nonlinear wave system J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-24 Hanze Liu, Adilai Yusupu
For dealing with exact solutions and properties of PDEs, the higher-dimensional equations are far more complicated than the lower dimensional ones. In the current paper, by using the dynamical system method, we study the -dimensional generalized nonlinear coupled KdV (c-KdV) system, which includes a lot of important c-KdV types of systems as its special case. The bifurcations and phase portraits of
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A family of [formula omitted] four-point stationary subdivision schemes with fourth-order accuracy and shape-preserving properties J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-23 Hyoseon Yang, Kyungmi Kim, Jungho Yoon
The four-point interpolatory scheme and the cubic B-spline are examples of the most well-known stationary subdivision procedures. They are based on the space of cubic polynomials and have their respective strengths and weaknesses. In this regard, the purpose of this study is to introduce a new type of subdivision scheme that integrates the advantages of both the four-point and the cubic B-spline schemes
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Automated importance sampling via optimal control for stochastic reaction networks: A Markovian projection–based approach J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-23 Chiheb Ben Hammouda, Nadhir Ben Rached, Raúl Tempone, Sophia Wiechert
We propose a novel alternative approach to our previous work (Ben Hammouda et al., 2023) to improve the efficiency of Monte Carlo (MC) estimators for rare event probabilities for stochastic reaction networks (SRNs). In the same spirit of Ben Hammouda et al. (2023), an efficient path-dependent measure change is derived based on a connection between determining optimal importance sampling (IS) parameters
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Quantile Hedging in the complete financial market under the mixed fractional Brownian motion model and the liquidity constraint J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-22 Bing Cui, Alireza Najafi
This article proposes a pricing framework for European option that utilizes a Quantile hedging strategy in a complete financial market. The methodology involves applying the long memory Geometric Brownian motion model, utilizing the generalized mixed fractional Girsanov theorem, and incorporating relevant findings related to quasi-conditional expectation. The first step in this framework is to derive
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Nonparametric modal regression with mixed variables and application to analyze the GDP data J. Comput. Appl. Math. (IF 2.4) Pub Date : 2024-02-22 Zhong-Cheng Han, Yan-Yong Zhao
Modal regression is as efficient as mean regression when the random error follows normal distribution, and is robust to the presence of outliers or skewed distributions. Due to such advantages as efficiency and robustness, it has been widely applied in different fields, such as medicine, economics, environment and so on. However, most existing literature based on modal regression are assumed that the