中国科学院数学与系统科学研究院期刊网

14 February 2025, Volume 47 Issue 1
    

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    Youth Review
  • Lai Jun, Zhang Jinrui
    Mathematica Numerica Sinica. 2025, 47(1): 1-20. https://doi.org/10.12286/jssx.j2024-1267
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    The Fast Multipole Method (FMM) is a highly efficient numerical algorithm for handling large-scale multi-particle systems, playing an important role in fields such as molecular dynamics, astrodynamics, acoustics, and electromagnetics. This paper first reviews the history of the Fast Multipole Method, then taking Helmholtz and Maxwell equations as examples, introduces the data structures, mathematical principles, implementation steps, and complexity analysis of the FMM based on kernel analytical expansion in two-dimensional and three-dimensional cases, and describes corresponding adaptive version of FMM. Finally, numerical experiments on multi-particle simulations in two-dimensional and three-dimensional spaces are given on the MATLAB platform.
  • Articles
  • Wang Danxia, Liu Jing
    Mathematica Numerica Sinica. 2025, 47(1): 21-36. https://doi.org/10.12286/jssx.j2022-0981
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    In this paper, we consider a numerical approximation for phase field model of nematic liquid crystal and viscous fluids. An equivalent model of the phase field model of nematic liquid crystal and viscous fluid is obtained based on the convex splitting strategy of the Ginzburg-Landau functional. In the numerical scheme, the backward Euler method is used for temporal discretization, and the hybrid finite element method is used for spacial discretization. Here the pressure correction method is used to decouple the computation of the pressure from that of the velocity. Hence, a new first-order scheme is proposed. This proposed scheme is unconditionally stable, as rigorously proven by theoretical analysis. In addition, numerical simulations are given on the temporal convergence rates with different parameters, the spacial convergence rates with different parameters, the evolution of energies, and the annihilation of singularities for the variables d, u, φ. Ample numerical simulations are performed to validate the accuracy and efficiency of the proposed scheme.
  • Hu Mengting, Deng Dingwen
    Mathematica Numerica Sinica. 2025, 47(1): 37-60. https://doi.org/10.12286/jssx.j2023-1154
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    This study focuses on the numerical solutions of the delayed Fisher’s equations by a class of non-negativity-preserving finite difference methods (FDMs) and a kind of maximumprinciple-satisfying FDMs. At first, by using a class of weighted difference formulas and explicit Euler method to discrete the diffusion term and first-order temporal derivative, respectively, a class of non-negativity-preserving FDMs are established for the delayed Fisher’s equations. Secondly, by applying cut-off technique to adjust the numerical solutions obtained by non-negativity-preserving FDMs, a kind of maximum-principle-satisfying FDMs are developed for the delayed Fisher’s equations. Thirdly, by using the non-negativity and boundedness of numerical and exact solutions, the maximum norm error estimations and stabilities for them are given, rigorously. Numerical results confirm the correctness of theoretical findings and the efficiency of the current methods.
  • Chen Yingzi, Wang Wansheng, Xie Jiaquan
    Mathematica Numerica Sinica. 2025, 47(1): 61-78. https://doi.org/10.12286/jssx.j2024-1175
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    In this manuscript we proposed using the implicit-explicit splitting method to solve the linear complementarity problem satisfied by American options in financial option pricing problems. Although implicit-explicit methods have been widely used in jump-diffusion models, they are mostly applied in European options, and there is little stability analysis in numerical solutions for American options. In this paper, we proposed that in terms of time, we adopted three discretization methods: the implicit-explicit Backward differential formula of order two (BDF2), the implicit-explicit Crank-Nikolson Leap-Frog(CNLF), and the implicit-explicit Crank-Nikolson AdamBashforth(CNAB), and proved their stability. In space, finite difference discretization is presented, and due to the nonsmoothness of the initial value function, a local mesh refinement strategy is considered near the strike price to improve accuracy. To verify the theoretical results, numerical results for pricing American options under Merton type and Kou type jump-diffusion models were presented. The numerical experimental results show that our proposed method is stable and effective.
  • Lyu Tong, Ye Xingyang
    Mathematica Numerica Sinica. 2025, 47(1): 79-97. https://doi.org/10.12286/jssx.j2024-1177
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    The Two-Step Backward Difference Formula (BDF2) of variable-step in time has exceptional stability, making it an excellent choice for handling stiff problems and multi-scale dynamics issues. However, there is limited research on the optimal control of Partial Differential Equations. This paper introduces a variable-step method to solve the optimal control problem of source term control for a class of reaction-diffusion equations. Specifically, the BDF2 scheme is employed in time, while in space, we utilize the center-difference method for variable-step difference scheme in the L2 norm, provided that the ratio of adjacent time-steps falls within the range of $\frac{1}{4.8645}$ to 4.8645. Furthermore, it achieves second-order convergence accuracy in both time and space. Finally, two numerical examples are provided to validate the feasibility and effectiveness of the proposed scheme.
  • Zhou Jing, Chen Xin, Zhou Xuelin, Li Jiaofen
    Mathematica Numerica Sinica. 2025, 47(1): 98-121. https://doi.org/10.12286/jssx.j2024-1179
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    Multidimensional scaling (MDS) is a data analysis technology that displays and analyzes the corresponding multidimensional data structure in the low-dimensional space. The Individual Difference Scaling (INDSCAL) is a specific model for simultaneous metric multidimensional scaling (MDS) of several data matrices, which not only analyzes the structure of the analysis object, but also takes into account the difference in scales between subjects. In the present work the orthogonal INDSCAL(O-INDSCAL) problem is considered and the problem of fitting the O-INDSCAL model is constructed as a matrix optimization model constrained by Stiefel manifold and linear manifolds. By leveraging the geometric properties of the product manifold, basing on the strong Wolfe line search, we design an adaptive extended hybrid Riemannian conjugate gradient algorithm for the underlying problem and its global convergence is further discussed. Numerical experiments demonstrate that the hybrid method is feasible and effective for the model. Moreover, the proposed algorithm exhibits certain advantages in terms of iterative efficiency compared to the algorithms in the Riemannian optimization toolbox and other Riemannian first-order algorithms.
  • Li Xuehua, Chen Linjie, Chen Cairong
    Mathematica Numerica Sinica. 2025, 47(1): 122-134. https://doi.org/10.12286/jssx.j2024-1182
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    In this paper, a monotone coordinate descent algorithm for solving absolute value equations is presented, and the global convergence of the algorithm is analyzed under appropriate conditions. The feasibility and effectiveness of the proposed algorithm are verified by numerical experiments. Another purpose of this paper is to point out a mistake in the paper by Noor et al. [Optim. Lett., 6:1027-1033, 2012], which is caused by misuse of the second-order Taylor expansion in constructing the descending direction of the objective function.
  • Tang Lingyan, Liu Tao, Wang Zhiyuan
    Mathematica Numerica Sinica. 2025, 47(1): 135-148. https://doi.org/10.12286/jssx.j2024-1186
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    A new high-order well-balanced finite difference scheme based on weighted compact nonlinear scheme (WCNS) is proposed for the Euler equation with gravitational source on the generalized coordinate system. The basic idea is to reconstruct the gravitational source term using steady-state solution, so that it can correspond to the pressure gradient at the lefthand-side of the equations in an equilibrium state. To ensure that the reconstructed value of the conserved variables is exactly equal to the reconstructed value of the steady-state solution in an equilibrium state, a nonlinear interpolation with scale invariance property is used in the reconstruction procedure. Since the same central difference scheme can be used for both flux derivatives and grid derivatives, the proposed scheme satisfy geometric conservation laws on curvilinear grids. By theoretical analysis and experimental results, it is indicated that the proposed WCNS scheme can preserve the general steady state which include both ispthermal and polytropic equilibria, and geometric conservation laws. Moreover, it can achieve fifth-order accuracy and capture exactly small perturbations near steady-state solutions on curvilinear grids.
  • Wang Chuanlong, Li Wenwei, Wen Ruiping, Zhao Peipei
    Mathematica Numerica Sinica. 2025, 47(1): 149-171. https://doi.org/10.12286/jssx.j2024-1187
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    In this paper, we established a new non-convex optimization based on L*-LF for low Tucker rank tensor completion problem. Three algorithms for solving the new optimization are designed based on the augmented Lagrange multiplier methods. In theory, we analyze the global convergence of the algorithms. In numerical experiment, the simulation data and real image inpainting for the new non-convex optimization and the traditional convex optimization based on nuclear norm are carried out. Experiments results show the new model outperform the nuclear norm model in CPU times under the same precision.
  • Wang Tanxing, Song Yongzhong, Cai Xingju
    Mathematica Numerica Sinica. 2025, 47(1): 172-190. https://doi.org/10.12286/jssx.j2024-1192
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    This paper considers a special nonconvex optimization problem, namely DC optimization problem, whose objective function can be written as the sum of a smooth convex function, a proper closed convex function and a continuous possibly nonsmooth concave function. This paper develops a general inertial proximal DC algorithm (GIPDCA), which adopts three different extrapolation points for the inertial direction and the gradient center and the proximal center in solving subproblems based on the classical proximal DC algorithm. The proposed GIPDCA can include some classical algorithms as special cases. Under the assumption that the objective function satisfies the Kurdyka-Łojasiewicz property and some suitable conditions on the parameters, we prove that each bounded sequence generated by GIPDCA globally converges to a critical point. In addition, numerical simulations demonstrate the feasibility and effectiveness of the proposed approach.