中国科学院数学与系统科学研究院期刊网

09 April 2026, Volume 48 Issue 2
    

  • Select all
    |
    Youth Review
  • Xu Yangyang
    Mathematica Numerica Sinica. 2026, 48(2): 211-225. https://doi.org/10.12286/jssx.j2025-1358
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    First-order methods play a central role in large-scale optimization due to their low per-iteration cost and scalability. Early research primarily focused on unconstrained problems or optimization problems with simple constraints. Motivated by the rapid growth of constrained machine learning and engineering applications, recent years have witnessed increasing interest in the design and theoretical analysis of first-order methods for functional constrained optimization problems, where constraints are defined through complex functions. This survey provides a systematic review of first-order algorithms for solving optimization problems with functional constraints. We cover a broad range of problem settings, including deterministic linearly constrained convex problems, deterministic nonlinearly constrained convex problems, problems with nonconvex objectives and convex constraints, fully nonconvex constrained problems, as well as stochastic optimization problems with both convex and nonconvex structures. For each class of problems, we summarize representative algorithms and their associated complexity guarantees, focusing on the number of iterations required to obtain either an $\epsilon$-optimal solution or an $\epsilon$-KKT point. By unifying existing results across different problem structures and algorithmic frameworks, this survey highlights current theoretical limits, identifies key assumptions such as constraint qualifications, and outlines promising directions for future research.
  • Articles
  • Lu Yanxin, Jia Xiaohong, Liu Xin
    Mathematica Numerica Sinica. 2026, 48(2): 226-248. https://doi.org/10.12286/jssx.j2025-1340
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Calculation of the convex hull function and detection of its nonsmooth points has widely applications in phase diagram calculation. This paper proposes an algorithm to calculate the convex hull function and detect its nonsmooth points of the pointwise minimum of finite smooth functions defined on the interval $[0,1]$. The algorithm discretizes the interval by uniformly selecting $(N+1)$ grid points, from which $\left(\frac{N}{m}+1\right)$ nodes are selected to construct the double-loop scanning process. It finds the numerical convex hull function by dynamically updating the expression of the objective function. The algorithm identifies the grid points at the junctions of adjacent segments of the numerical convex hull function as nonsmooth points. The computational complexity of the algorithm is $\mathcal{O}\left(\frac{N^3}{m^2}\right)$, and the theoretical error order is $\mathcal{O}\left(\frac{m}{N}\right)$. We also investigate an improved strategy, in which different nodes are used in different times of scanning. Numerical experiments confirm the theoretical error and computational complexity, and demonstrate that the improved strategy of the algorithm enhances numerical performance in proper parameter settings.
  • Liang Xiaoyu, Zhao Zhihui, Li Hong
    Mathematica Numerica Sinica. 2026, 48(2): 249-264. https://doi.org/10.12286/jssx.j2025-1320
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In this paper, we study the space-time finite element method based on the Petrov-Galerkin approximation for the nonlinear reaction-diffusion. Firstly, we construct its space-time finite element scheme, and then we prove the existence and uniqueness as well as give the convergence analysis of the numerical solution without the restrictions of stability conditions. In contrast to the conventional finite element method, this method is convenient to obtain high order accuracy both in space and time directions as well as has good numerical stability. At last, some numerical tests are provided to validate the correction of the theoretical results and the effectiveness of the method. Also, it is shown that the space-time Petrov-Glerkin (STPG) method is more efficient than the space-time Galerkin (STG) method in obtaining nearly the same error and convergence order.
  • Wang Shengzhe, Gong Dianxuan, Wei Fengfan, Li Haoqi
    Mathematica Numerica Sinica. 2026, 48(2): 265-280. https://doi.org/10.12286/jssx.j2025-1321
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In order to solve the ill-posed matrix problem caused by the small support radius in Compact-supported Radial Basis Function (CSRBF) interpolation, and the limitation of the existing methods that it is difficult to optimize the key parameters jointly, a regularized Wendland-polynomial combination interpolation model was proposed. The proposed model combines Wendland radial basis and polynomial basis, and imposes selective Tikhonov regularization (parameter $\lambda$) on the polynomial coefficients through the block matrix structure to suppress ill-posedness and improve stability. At the same time, an adaptive hybrid random walk algorithm (IAH-RW) was designed to optimize the support radius and polynomial order. Numerical experiments show that the accuracy of the combined model is better than the traditional RBF or polynomial interpolation in complex edge reconstruction and local detail recovery, and IAH-RW algorithm improves the accuracy and stability of parameter optimization.
  • Cao Yang, Wang Luxin, Yang Aili, Zhou Chencan
    Mathematica Numerica Sinica. 2026, 48(2): 281-294. https://doi.org/10.12286/jssx.j2025-1322
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Converting the legalization problem of integrated circuit layout design into a linear complementary problem with the system matrix being of saddle point matrix structure has many advantages, and the efficient numerical solution algorithm of the latter is closely related to the eigenvalues distribution of the saddle point matrix. In the case where the standard cell has a single row height, we analyze in detail the eigenvalues estimation of the saddle point matrix. Theoretical analysis indicates that some eigenvalues of this kind of saddle point matrices are 1, and the remaining eigenvalues are distributed on two mutually perpendicular line segments centered on $(\frac{1}{2},0)$ in the complex plane. Moreover, we provide the upper and lower bounds of both the real eigenvalues and the imaginary parts of complex eigenvalues. Finally, these theoretical results are verified through three numerical examples.
  • Yang Jinping, Yang Yan, Li Zhiqiang, Yan Yubin
    Mathematica Numerica Sinica. 2026, 48(2): 295-314. https://doi.org/10.12286/jssx.j2025-1323
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In this paper, we study a numerical algorithm for a class of nonlinear stochastic time-fractional subdiffusion equation driven by multiplicative noise. The spatial derivative is approximated by applying the spectral Galerkin method and the error estimate of the semidiscrete scheme is established. The fully discrete scheme of the equation is proposed by using the Mittag-Leffler Euler method and the error estimates of strong convergence of the schemes are presented based on the boundedness and regularity of the numerical solutions. The results of numerical experiments verify that the proposed algorithm is efficient.
  • Shen Jialu, Huang Zhongyi, Yang Wenli
    Mathematica Numerica Sinica. 2026, 48(2): 315-338. https://doi.org/10.12286/jssx.j2025-1326
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    Sparse signal and block-based compressed image recovery are key research areas in sparse optimization, typically referring to the process of reconstructing the original function or image from sparsely sampled or compressed noisy observations. Considering non-uniform discrete Fourier sampling, this paper extends the nonconvex $\ell_0$ minimization model to the complex domain and proposes a two-stage compression recovery method based on nonconvex regularization and log-TV regularization terms for $\ell_0$ norm optimization. This method improves computational efficiency while maintaining accuracy, effectively eliminating the staircase effect in piecewise smooth function and block-based compressed image recovery. We analyze the convergence and solution properties of the proposed algorithm and compare it with other iterative algorithms and block-based compressed image reconstruction methods in numerical experiments. The experimental results reflect the characteristics of the proposed model and verify the effectiveness of the algorithm.
  • Zhang Mengyu, Liu Weizhen, Tang Qinglin
    Mathematica Numerica Sinica. 2026, 48(2): 339-350. https://doi.org/10.12286/jssx.j2025-1327
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    This paper proposes a plug-and-play acceleration method based on gradient-free optimization for steady-state thermo-mechanical coupling problems in fuel rod cladding. By integrating gradient-free hyperparameter optimization into conventional numerical workflows, we achieve automated parameter selection for the Hypre-BoomerAMG preconditioner. The study first identifies optimal preconditioning configurations through representative parameter sets, then verifies their stable acceleration performance across diverse material parameters. Numerical experiments demonstrate that the proposed method maintains significant acceleration effects under varying physical scenarios while exhibiting superior engineering robustness. This work establishes a reproducible and transferable gradient-free acceleration framework for large-scale multi-physics simulations, with extensibility to transient analyses, elastoplastic contact problems, and multi-objective optimization in heterogeneous hardware environments.
  • Pang Chunping, Liu Wenjie, Zhang Xuelin, Wang Hanquan
    Mathematica Numerica Sinica. 2026, 48(2): 351-366. https://doi.org/10.12286/jssx.j2025-1329
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    In recent years, significant progress has been made in numerical studies of ground-state solutions for Bose-Einstein condensates. Building upon previous research, this paper proposes a novel computational scheme: first, the constrained energy functional minimization problem in function space is transformed into an unconstrained optimization problem in parameter space via a reparameterization approach; then, by incorporating derivative approximation and numerical integration techniques, the continuous optimization problem is discretized into a standard finite-dimensional formulation; finally, the quasi-Newton method is employed for efficient numerical solution. Systematic numerical experiments in one, two, and three dimensions validate the computational accuracy and effectiveness of the proposed method. The results demonstrate that the reparameterization scheme accurately captures the spatial localization characteristics of quantum systems when solving the ground-state problem of Bose-Einstein condensates, exhibiting excellent numerical stability and broad applicability.
  • Chen Leyu, Wang Kun, Liu Tiegang, Liu Jinpeng, Xu Liang
    Mathematica Numerica Sinica. 2026, 48(2): 367-394. https://doi.org/10.12286/jssx.j2025-1333
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    This paper proposes a quantum computing method for solving linear partial differential equations (PDEs), leveraging the Linear Combination of Hamiltonian Simulations (LCHS) framework for ordinary differential equations (ODEs) combined with matrix representations of differential operators. First, the PDE is transformed into a system of ODEs via spatial discretization using finite differences. For the matrix representations of various differential operators under specific boundary conditions, corresponding quantum circuits for Hamiltonian simulation are designed. The potential advantage of this method over classical numerical approaches is analyzed from the perspective of computational complexity. Subsequently, taking the heat equation (parabolic type) and advection equation (hyperbolic type) as illustrative examples, the complete quantum solving workflow with circuit implementation details are demonstrated. Finally, the effectiveness of the method is validated through numerical experiments.