Journal of Computational and Applied Mathematics最新文献_第6页

Dynamics analysis of a discrete diffusion predator–prey model with prey refuge 具有猎物庇护的离散扩散捕食者-猎物模型动力学分析

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-18 DOI: 10.1016/j.cam.2025.116706

Xiongxiong Du, Xiaoling Han

引用次数: 0

A nodally bound-preserving finite element method for time-dependent convection–diffusion equations 时变对流扩散方程的节点保界有限元方法

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-17 DOI: 10.1016/j.cam.2025.116691

Abdolreza Amiri , Gabriel R. Barrenechea , Tristan Pryer

引用次数: 0

Sampling with derivatives in finitely generated shift-invariant spaces 有限生成平移不变空间中的导数抽样

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-17 DOI: 10.1016/j.cam.2025.116692

Kumari Priyanka, A. Antony Selvan

引用次数: 0

Fuzzy random multi-objective optimization using a novel mixed fuzzy random inverse DEA model in input-output production 基于混合模糊随机逆DEA模型的投入产出生产模糊随机多目标优化

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-15 DOI: 10.1016/j.cam.2025.116717

Lizhen Huang, Lei Chen

{"title":"Fuzzy random multi-objective optimization using a novel mixed fuzzy random inverse DEA model in input-output production","authors":"Lizhen Huang, Lei Chen","doi":"10.1016/j.cam.2025.116717","DOIUrl":"10.1016/j.cam.2025.116717","url":null,"abstract":"<div><div>Inverse data envelopment analysis (DEA), which is an effective tool for determining inputs and outputs, is commonly applied in areas such as output prediction, resource allocation, and target setting. However, existing inverse DEA methods typically assume precise and deterministic data, which limits applicability in uncertain production environments, particularly when both random and fuzzy environments are present. This study introduces a novel inverse DEA approach for optimizing inputs and outputs in mixed uncertainty environments. The proposed model allows decision-makers to achieve target efficiency and meet various input/output targets under different production scale assumptions. First, a new optimality principle for multi-objective fuzzy random problems is presented and the necessary theoretical conditions for input/output calculations are derived. Second, an equivalent linear model is introduced to solve the inverse DEA problem with fuzzy random variables, thereby overcoming the challenges associated with nonlinear programming. Notably, the proposed model offers enhanced flexibility as it does not rely on specific fuzzy numbers or predefined assumptions regarding random distributions. Finally, the effectiveness of the model is validated through numerical examples and a case study, demonstrating its practical application in complex decision-making scenarios.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"470 ","pages":"Article 116717"},"PeriodicalIF":2.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851755","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Longitudinal quantile-based regression models using multivariate asymmetric heavy-tailed distributions and leapfrog HMC algorithm 基于多变量非对称重尾分布和跨越式HMC算法的纵向分位数回归模型

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-15 DOI: 10.1016/j.cam.2025.116690

Maryam Sabetrasekh, Iraj Kazemi

{"title":"Longitudinal quantile-based regression models using multivariate asymmetric heavy-tailed distributions and leapfrog HMC algorithm","authors":"Maryam Sabetrasekh, Iraj Kazemi","doi":"10.1016/j.cam.2025.116690","DOIUrl":"10.1016/j.cam.2025.116690","url":null,"abstract":"<div><div>Modeling longitudinal quantile regression in a multivariate framework poses particular computational and conceptual challenges due to the inherent complexity of multivariate dependencies. While univariate quantile-based models can be extended to higher dimensions via Gram-Schmidt orthogonalization, such extensions often have practical limitations. To address these challenges, this paper introduces a flexible family of multivariate asymmetric distributions using the probabilistic Rosenblatt transformation. This framework preserves conditional coherence across longitudinal quantile processes via a sequential likelihood factorization, provides an explicit characterization of quantiles shaped by distributional asymmetry and covariate effects, controls for the influence of outliers, and improves computational efficiency in the estimation process. For Bayesian inference, we implement a leapfrog Hamiltonian Monte Carlo algorithm with the No-U-Turn Sampler to estimate longitudinal quantile-based regression parameters. Simulation studies over various quantile levels demonstrate the method’s theoretical properties, while two empirical applications highlight its practical utility and superior performance.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"470 ","pages":"Article 116690"},"PeriodicalIF":2.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An efficient first-order conditional gradient algorithm in data-driven sparse identification of nonlinear dynamics to solve sparse recovery problems under noise 一种有效的一阶条件梯度算法在数据驱动的非线性动力学稀疏识别中解决噪声下的稀疏恢复问题

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-15 DOI: 10.1016/j.cam.2025.116675

Alejandro Carderera , Sebastian Pokutta , Christof Schütte , Martin Weiser

{"title":"An efficient first-order conditional gradient algorithm in data-driven sparse identification of nonlinear dynamics to solve sparse recovery problems under noise","authors":"Alejandro Carderera , Sebastian Pokutta , Christof Schütte , Martin Weiser","doi":"10.1016/j.cam.2025.116675","DOIUrl":"10.1016/j.cam.2025.116675","url":null,"abstract":"<div><div>Governing equations are essential to the study of nonlinear dynamics, often enabling the prediction of previously unseen behaviors as well as the inclusion into control strategies. The discovery of governing equations from data thus has the potential to transform data-rich fields where well-established dynamical models remain unknown. This work contributes to the recent trend in data-driven sparse identification of nonlinear dynamics of finding the best sparse fit to observational data in a large library of potential nonlinear models. We propose an efficient first-order Conditional Gradient algorithm for solving the underlying optimization problem. In comparison to the most prominent alternative framework, the new framework shows significantly improved performance on several essential issues like sparsity-induction, structure-preservation, noise robustness, and sample efficiency. We demonstrate these advantages on several dynamics from the field of synchronization, particle dynamics, and enzyme chemistry.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"470 ","pages":"Article 116675"},"PeriodicalIF":2.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143899281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint group sparse coding and weighted nuclear norm for Poisson denoising 泊松去噪的联合群稀疏编码和加权核范数

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-14 DOI: 10.1016/j.cam.2025.116695

Jianguang Zhu , Wen Gao , Ying Wei , Binbin Hao

引用次数: 0

A fast solver for pentadiagonal Toeplitz systems with multiple right-hand sides 具有多个右手边的五对角Toeplitz系统的快速求解器

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-13 DOI: 10.1016/j.cam.2025.116697

Hcini Fahd

引用次数: 0

High-order implicit maximum-principle-preserving local discontinuous Galerkin methods for convection–diffusion equations 对流扩散方程的高阶隐式保极大原理局部不连续Galerkin方法

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-12 DOI: 10.1016/j.cam.2025.116660

Kaichang Yu , Juan Cheng , Yuanyuan Liu , Chi-Wang Shu

{"title":"High-order implicit maximum-principle-preserving local discontinuous Galerkin methods for convection–diffusion equations","authors":"Kaichang Yu , Juan Cheng , Yuanyuan Liu , Chi-Wang Shu","doi":"10.1016/j.cam.2025.116660","DOIUrl":"10.1016/j.cam.2025.116660","url":null,"abstract":"<div><div>We consider maximum-principle-preserving (MPP) property of two types of implicit local discontinuous Galerkin (LDG) schemes for solving diffusion and convection–diffusion equations. The first one is the original LDG scheme proposed in Cockburn and Shu (1998) with backward Euler time discretization. The second one adds an MPP scaling limiter defined in Zhang and Shu (2010), to the first one. Compared with explicit time discretization, implicit method allows for a larger time step. For pure diffusion equations in 1D, we prove that the second type of the LDG schemes is MPP, which can also achieve high order accuracy. This result can be generalized to 2D by using tensor product meshes but only for the second order <span><math><msup><mrow><mi>Q</mi></mrow><mrow><mn>1</mn></mrow></msup></math></span> case. For convection–diffusion equations, the first type of LDG schemes, in the second order <span><math><msup><mrow><mi>P</mi></mrow><mrow><mn>1</mn></mrow></msup></math></span> case in 1D, is proved to be MPP. In all the results above, in order to achieve the MPP property, it is necessary to have a lower bound on the time step in terms of the Courant–Friedrichs–Lewy (CFL) number. Although the analysis is only performed on linear equations, numerical experiments are provided to demonstrate that the second type of the LDG schemes works well in terms of the MPP property both for nonlinear convection–diffusion equations and for 2D higher order cases.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"470 ","pages":"Article 116660"},"PeriodicalIF":2.1,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Augmented Levin methods for vector-valued highly oscillatory integrals with exotic oscillators and turning points 具有奇异振子和拐点的向量值高振荡积分的增广Levin方法

IF 2.1 2区数学

Journal of Computational and Applied Mathematics Pub Date : 2025-04-12 DOI: 10.1016/j.cam.2025.116687

Yinkun Wang , Shuhuang Xiang

引用次数: 0