Pub Date : 2026-10-01Epub Date: 2026-02-09DOI: 10.1016/j.cam.2026.117429
Zhenxian Luo , Haijun Xia , Linyuan Li
The topology optimization of continuum structures considering stress constraints is a classic hotspot. Recently, the Stress-Influence-Function with adaptive strength feature (SIF-ASF) approach was proposed for stress constrained continuum topology optimization. The stress influence function sets strong penalization on the local strength failure to achieve the stress constraints. However, this strong penalization may lead to oscillation or divergence due to a sharp barrier of the stress. In this study, an improved stress influence function, which has good boundedness and smoothness, is presented to alleviate nonlinearity in optimization and ensure numerical stability of optimization iterations. In addition, a new adaptive strategy for the strength feature factor is proposed to achieve good control on the maximum stress. By comparing with existing methods through two numerical examples, the advantages of the proposed method on numerical stability and weight reduction are verified. Finally, some useful conclusions are given objectively.
{"title":"An improved Stress-Influence-Function (ISIF) based method for continuum structural topology optimization with stress constraints","authors":"Zhenxian Luo , Haijun Xia , Linyuan Li","doi":"10.1016/j.cam.2026.117429","DOIUrl":"10.1016/j.cam.2026.117429","url":null,"abstract":"<div><div>The topology optimization of continuum structures considering stress constraints is a classic hotspot. Recently, the Stress-Influence-Function with adaptive strength feature (SIF-ASF) approach was proposed for stress constrained continuum topology optimization. The stress influence function sets strong penalization on the local strength failure to achieve the stress constraints. However, this strong penalization may lead to oscillation or divergence due to a sharp barrier of the stress. In this study, an improved stress influence function, which has good boundedness and smoothness, is presented to alleviate nonlinearity in optimization and ensure numerical stability of optimization iterations. In addition, a new adaptive strategy for the strength feature factor is proposed to achieve good control on the maximum stress. By comparing with existing methods through two numerical examples, the advantages of the proposed method on numerical stability and weight reduction are verified. Finally, some useful conclusions are given objectively.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117429"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387131","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}
Pub Date : 2026-10-01Epub Date: 2026-02-26DOI: 10.1016/j.cam.2026.117495
Minsheng Huang , Ruo Li , Kai Yan , Chengbao Yao , Wenjun Ying
The reference field method, known as the difference formulation, is a key variance reduction technique for Monte Carlo simulations of thermal radiation transport problems. When the material temperature is relatively high and the spatial temperature gradient is moderate, this method demonstrates significant advantages in reducing variance compared to classical Monte Carlo methods. However, in problems with larger temperature gradients, this method has not only been found ineffective at reducing statistical noise, but in some cases, it even increases noise compared to classical Monte Carlo methods. The global optimal reference field method, a recently proposed variance reduction technique, effectively reduces the average energy weight of Monte Carlo particles, thereby decreasing variance. Its effectiveness has been validated both theoretically and numerically, demonstrating a significant reduction in statistical errors for problems with large temperature gradients. In our previous work, instead of computing the exact global optimal reference field, we developed an approximate, physically motivated method to find a relatively better reference field using a selection scheme. In this work, we reformulate the problem of determining the global optimal reference field as a linear programming problem and solve it exactly. To further enhance computational efficiency, we use the MindOpt solver, which leverages graph neural network methods. Numerical experiments demonstrate that the MindOpt solver not only solves linear programming problems accurately but also significantly outperforms the Simplex and interior-point methods in terms of computational efficiency. The global optimal reference field method combined with the MindOpt solver not only improves computational efficiency but also substantially reduces statistical errors.
{"title":"An efficient Monte Carlo simulation for radiation transport based on global optimal reference field","authors":"Minsheng Huang , Ruo Li , Kai Yan , Chengbao Yao , Wenjun Ying","doi":"10.1016/j.cam.2026.117495","DOIUrl":"10.1016/j.cam.2026.117495","url":null,"abstract":"<div><div>The reference field method, known as the difference formulation, is a key variance reduction technique for Monte Carlo simulations of thermal radiation transport problems. When the material temperature is relatively high and the spatial temperature gradient is moderate, this method demonstrates significant advantages in reducing variance compared to classical Monte Carlo methods. However, in problems with larger temperature gradients, this method has not only been found ineffective at reducing statistical noise, but in some cases, it even increases noise compared to classical Monte Carlo methods. The global optimal reference field method, a recently proposed variance reduction technique, effectively reduces the average energy weight of Monte Carlo particles, thereby decreasing variance. Its effectiveness has been validated both theoretically and numerically, demonstrating a significant reduction in statistical errors for problems with large temperature gradients. In our previous work, instead of computing the exact global optimal reference field, we developed an approximate, physically motivated method to find a relatively better reference field using a selection scheme. In this work, we reformulate the problem of determining the global optimal reference field as a linear programming problem and solve it exactly. To further enhance computational efficiency, we use the MindOpt solver, which leverages graph neural network methods. Numerical experiments demonstrate that the MindOpt solver not only solves linear programming problems accurately but also significantly outperforms the Simplex and interior-point methods in terms of computational efficiency. The global optimal reference field method combined with the MindOpt solver not only improves computational efficiency but also substantially reduces statistical errors.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117495"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386903","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}
Pub Date : 2026-10-01Epub Date: 2026-02-21DOI: 10.1016/j.cam.2026.117474
Fangfang Shi , Guoju Ye , Wei Liu , Debdas Ghosh
The main objective of this paper is to investigate the KKT optimality condition for fuzzy optimization problems with inequality constraints. To begin with, by proving that the intersection of the cone of descent directions and the cone of feasible directions at the optimal point is an empty set, we establish the first-order optimality condition for unconstrained fuzzy optimization problems. On this basis, the Fritz-John optimality condition for fuzzy optimization problems with inequality constraints is derived through the fuzzy Gordan’s theorem. Furthermore, in order to ensure that the Lagrangian multipliers must satisfy not all zero, we strengthen the assumptions to deduce the KKT optimality condition. Meanwhile, some numerical examples are created to verify the validity of theoretical results. It is particularly worth mentioning that the optimality conditions established in this paper are such that zero belongs to a certain interval, which makes our results computationally superior than in the previous literature, where the optimality conditions are equalities. Finally, the developed optimality conditions are employed to address a binary classification problem related to support vector machines with fuzzy data.
{"title":"Optimality conditions for fuzzy optimization problems and its application to classification problems with fuzzy data","authors":"Fangfang Shi , Guoju Ye , Wei Liu , Debdas Ghosh","doi":"10.1016/j.cam.2026.117474","DOIUrl":"10.1016/j.cam.2026.117474","url":null,"abstract":"<div><div>The main objective of this paper is to investigate the KKT optimality condition for fuzzy optimization problems with inequality constraints. To begin with, by proving that the intersection of the cone of descent directions and the cone of feasible directions at the optimal point is an empty set, we establish the first-order optimality condition for unconstrained fuzzy optimization problems. On this basis, the Fritz-John optimality condition for fuzzy optimization problems with inequality constraints is derived through the fuzzy Gordan’s theorem. Furthermore, in order to ensure that the Lagrangian multipliers must satisfy not all zero, we strengthen the assumptions to deduce the KKT optimality condition. Meanwhile, some numerical examples are created to verify the validity of theoretical results. It is particularly worth mentioning that the optimality conditions established in this paper are such that zero belongs to a certain interval, which makes our results computationally superior than in the previous literature, where the optimality conditions are equalities. Finally, the developed optimality conditions are employed to address a binary classification problem related to support vector machines with fuzzy data.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117474"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386992","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}
Pub Date : 2026-10-01Epub Date: 2026-02-27DOI: 10.1016/j.cam.2026.117492
Xiao Peng , Yijing Wang , Zhiqiang Zuo
This paper explores the asymptotic leader-follower consensus and Mittag-Leffler leader-follower consensus for variable-order multi-agent systems in the presence of unknown nonlinearity and external disturbances. Under the fixed/switching topologies, sufficient consensus criteria are respectively developed by proposing non-switched/switched distributed adaptive neural network-based dynamic event-trigger control schemes. At the end of this paper, some numerical simulations and comparison results are presented to imply the effectiveness of the proposed control strategies.
{"title":"Leader-follower consensus for variable-order multi-agent systems with fixed/switching topologies","authors":"Xiao Peng , Yijing Wang , Zhiqiang Zuo","doi":"10.1016/j.cam.2026.117492","DOIUrl":"10.1016/j.cam.2026.117492","url":null,"abstract":"<div><div>This paper explores the asymptotic leader-follower consensus and Mittag-Leffler leader-follower consensus for variable-order multi-agent systems in the presence of unknown nonlinearity and external disturbances. Under the fixed/switching topologies, sufficient consensus criteria are respectively developed by proposing non-switched/switched distributed adaptive neural network-based dynamic event-trigger control schemes. At the end of this paper, some numerical simulations and comparison results are presented to imply the effectiveness of the proposed control strategies.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117492"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387000","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}
In this paper, we study the steepest descent method for unconstrained optimization problems involving quasiconvex fuzzy objective functions under granular differentiability. We introduce a class of granular quasiconvex and pseudoconvex functions, referred to as gr-quasiconvexity and gr-pseudoconvexity. Key properties of these functions and their interrelations are discussed. Leveraging the theory of quasi-Fejr convergence, we prove that the sequence generated by the steepest descent method with a generalized Armijo search converges completely to a granular stationary point of the fuzzy optimization problem. Several numerical examples are provided to demonstrate the effectiveness of the proposed approach. Additionally, a potential application in finance is considered and solved using our method.
{"title":"Steepest descent method with a generalized Armijo search to solve quasiconvex fuzzy optimization problems under granular differentiability","authors":"Shenglan Chen , Li Zhong , Zengbao Wu , Changjie Fang","doi":"10.1016/j.cam.2026.117432","DOIUrl":"10.1016/j.cam.2026.117432","url":null,"abstract":"<div><div>In this paper, we study the steepest descent method for unconstrained optimization problems involving quasiconvex fuzzy objective functions under granular differentiability. We introduce a class of granular quasiconvex and pseudoconvex functions, referred to as <em>gr</em>-quasiconvexity and <em>gr</em>-pseudoconvexity. Key properties of these functions and their interrelations are discussed. Leveraging the theory of quasi-Fej<span><math><mover><mi>e</mi><mo>´</mo></mover></math></span>r convergence, we prove that the sequence generated by the steepest descent method with a generalized Armijo search converges completely to a granular stationary point of the fuzzy optimization problem. Several numerical examples are provided to demonstrate the effectiveness of the proposed approach. Additionally, a potential application in finance is considered and solved using our method.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117432"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387186","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}
Pub Date : 2026-10-01Epub Date: 2026-02-17DOI: 10.1016/j.cam.2026.117448
Qiumei Huang , Cheng Wang , Gangfan Zhong
In this paper, we propose two multi-step, linearized numerical schemes for a nonlinear convection-diffusion-reaction (CDR) equation with vanishing delay, a temporally nonlocal partial differential equation. These semi-implicit numerical schemes use a combination of explicit Adams–Bashforth extrapolation for the nonlinear term and implicit Adams–Moulton interpolation for the diffusion term. A long stencil finite difference approximation is employed for the spatial discretization, and a boundary extrapolation is used to prescribe the solution at “ghost” points lying outside of the computational domain. The numerical stability and convergence analysis is provided, and the discrete ℓ2 convergence estimate is obtained, with fourth-order spatial accuracy and high-order (third- or fourth-order) temporal accuracy. A few numerical experiments are also presented to confirm the theoretical results.
{"title":"Extended multi-step high-order numerical methods for the nonlinear convection-diffusion-reaction equation with vanishing delay","authors":"Qiumei Huang , Cheng Wang , Gangfan Zhong","doi":"10.1016/j.cam.2026.117448","DOIUrl":"10.1016/j.cam.2026.117448","url":null,"abstract":"<div><div>In this paper, we propose two multi-step, linearized numerical schemes for a nonlinear convection-diffusion-reaction (CDR) equation with vanishing delay, a temporally nonlocal partial differential equation. These semi-implicit numerical schemes use a combination of explicit Adams–Bashforth extrapolation for the nonlinear term and implicit Adams–Moulton interpolation for the diffusion term. A long stencil finite difference approximation is employed for the spatial discretization, and a boundary extrapolation is used to prescribe the solution at “ghost” points lying outside of the computational domain. The numerical stability and convergence analysis is provided, and the discrete ℓ<sup>2</sup> convergence estimate is obtained, with fourth-order spatial accuracy and high-order (third- or fourth-order) temporal accuracy. A few numerical experiments are also presented to confirm the theoretical results.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117448"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147387187","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}
Pub Date : 2026-10-01Epub Date: 2026-02-28DOI: 10.1016/j.cam.2026.117505
Qingqing Fu , Gang Cai , Zhongbing Xie , Qiao-Li Dong
In this paper, we mainly introduce a Halpern-type splitting algorithm with inertial extrapolation for approximating a common solution of monotone inclusion problems and equilibrium problems in reflexive Banach spaces. Our algorithm has a novel step-size rule which is designed by the golden ratio . The strong convergence results for the proposed algorithm are established under some reasonable assumptions imposed on the operators and the parameters. Furthermore, we give two interesting corollaries based on this algorithm. Finally, several numerical experiments are presented to demonstrate the efficiency and advantages of our proposed algorithm. The results obtained in this paper improve and generalize many recent ones in the literature.
{"title":"Halpern-type splitting algorithm for approximating a common solution of monotone inclusion problems and equilibrium problems in reflexive Banach spaces","authors":"Qingqing Fu , Gang Cai , Zhongbing Xie , Qiao-Li Dong","doi":"10.1016/j.cam.2026.117505","DOIUrl":"10.1016/j.cam.2026.117505","url":null,"abstract":"<div><div>In this paper, we mainly introduce a Halpern-type splitting algorithm with inertial extrapolation for approximating a common solution of monotone inclusion problems and equilibrium problems in reflexive Banach spaces. Our algorithm has a novel step-size rule which is designed by the golden ratio <span><math><mrow><mo>(</mo><msqrt><mn>5</mn></msqrt><mo>+</mo><mn>1</mn><mo>)</mo><mo>/</mo><mn>2</mn></mrow></math></span>. The strong convergence results for the proposed algorithm are established under some reasonable assumptions imposed on the operators and the parameters. Furthermore, we give two interesting corollaries based on this algorithm. Finally, several numerical experiments are presented to demonstrate the efficiency and advantages of our proposed algorithm. The results obtained in this paper improve and generalize many recent ones in the literature.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117505"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386898","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}
Pub Date : 2026-10-01Epub Date: 2026-02-22DOI: 10.1016/j.cam.2026.117491
Longfei Wang , Yong Xia , Yunhai Xiao
We address a class of optimization problems, denoted by (SFC), of minimizing the sum of a convex-concave fraction and a convex function over a convex set. It is shown that problem (SFC) can be reformulated into an equivalent one-dimensional optimization problem, where each subproblem is evaluated by solving an associated convex programming. The optimal Lagrangian multipliers of the convex subproblems are utilized to construct sawtooth-curve and wave-curve lower bounds, which play a crucial role in devising the branch-and-bound algorithm for globally solving (SFC). In this paper, we propose a two-layer dual approach to get hidden sawtooth-curve lower bounds, which leads to a new efficient branch-and-bound algorithm for solving (SFC). Moreover, we improve the iterative complexity with wave-curve bounds to for finding an ϵ-approximate optimal solution. Numerical results demonstrate that it is more efficient than the recent branch-and-bound algorithm based on wave-curve bounds.
{"title":"Global optimization of a convex-concave fraction plus a convex function using hidden sawtooth-curve bounds via a two-layer dual approach","authors":"Longfei Wang , Yong Xia , Yunhai Xiao","doi":"10.1016/j.cam.2026.117491","DOIUrl":"10.1016/j.cam.2026.117491","url":null,"abstract":"<div><div>We address a class of optimization problems, denoted by (SFC), of minimizing the sum of a convex-concave fraction and a convex function over a convex set. It is shown that problem (SFC) can be reformulated into an equivalent one-dimensional optimization problem, where each subproblem is evaluated by solving an associated convex programming. The optimal Lagrangian multipliers of the convex subproblems are utilized to construct sawtooth-curve and wave-curve lower bounds, which play a crucial role in devising the branch-and-bound algorithm for globally solving (SFC). In this paper, we propose a two-layer dual approach to get hidden sawtooth-curve lower bounds, which leads to a new efficient branch-and-bound algorithm for solving (SFC). Moreover, we improve the iterative complexity <span><math><mrow><mi>O</mi><mo>(</mo><mfrac><mn>1</mn><mi>ϵ</mi></mfrac><mo>)</mo></mrow></math></span> with wave-curve bounds to <span><math><mrow><mi>O</mi><mo>(</mo><mfrac><mn>1</mn><msqrt><mi>ϵ</mi></msqrt></mfrac><mo>)</mo></mrow></math></span> for finding an ϵ-approximate optimal solution. Numerical results demonstrate that it is more efficient than the recent branch-and-bound algorithm based on wave-curve bounds.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117491"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386990","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}
Pub Date : 2026-10-01Epub Date: 2026-02-21DOI: 10.1016/j.cam.2026.117497
Hanjie Liu , Yuanguo Zhu , Liu He , Zihan Qin
How to reallocate the cost reasonably among participants in the production process is a research topic that attracts much attention. In this paper, a new production possibility set is defined for the first time by using chance constraint under the premise that the inputs and outputs of decision-making units (DMUs) are regarded as uncertain variables. An uncertain data envelopment analysis (DEA) model is developed to evaluate the efficiency performance of DMUs, and the model is improved to enhance its ability to distinguish efficient DMUs. Given the competitive landscape among DMUs, a cost reallocation problem based on the efficiency of DMUs is studied. Initially, we construct an optimization model aimed at maximizing DMU’s efficiency, allowing each DMU to propose an initial efficiency evaluation proposal that maximizes its own interests, which is usually not satisfied by all DMUs. Consequently, we present an uncertain bargaining game model, through which the efficiency evaluation proposals of each DMU are continuously adjusted until a consensus is reached that satisfies all DMUs. Moreover, we also provide deterministic forms for all relevant models and verify their feasibility. Then, we design a bargaining game algorithm to determine the final efficiency evaluation proposal. We prove the convergence of this algorithm and demonstrate that the obtained efficiency evaluation proposal constitutes a Nash equilibrium solution. Finally, a classic numerical example is used to illustrate the effectiveness of the proposed method. Compared with the existing efficiency evaluation methods for dealing with data uncertainty and cost allocation methods, the proposed method shows significant superiority.
{"title":"A bargaining game approach for cost reallocation within an uncertain DEA model under chance constraints","authors":"Hanjie Liu , Yuanguo Zhu , Liu He , Zihan Qin","doi":"10.1016/j.cam.2026.117497","DOIUrl":"10.1016/j.cam.2026.117497","url":null,"abstract":"<div><div>How to reallocate the cost reasonably among participants in the production process is a research topic that attracts much attention. In this paper, a new production possibility set is defined for the first time by using chance constraint under the premise that the inputs and outputs of decision-making units (DMUs) are regarded as uncertain variables. An uncertain data envelopment analysis (DEA) model is developed to evaluate the efficiency performance of DMUs, and the model is improved to enhance its ability to distinguish efficient DMUs. Given the competitive landscape among DMUs, a cost reallocation problem based on the efficiency of DMUs is studied. Initially, we construct an optimization model aimed at maximizing DMU’s efficiency, allowing each DMU to propose an initial efficiency evaluation proposal that maximizes its own interests, which is usually not satisfied by all DMUs. Consequently, we present an uncertain bargaining game model, through which the efficiency evaluation proposals of each DMU are continuously adjusted until a consensus is reached that satisfies all DMUs. Moreover, we also provide deterministic forms for all relevant models and verify their feasibility. Then, we design a bargaining game algorithm to determine the final efficiency evaluation proposal. We prove the convergence of this algorithm and demonstrate that the obtained efficiency evaluation proposal constitutes a Nash equilibrium solution. Finally, a classic numerical example is used to illustrate the effectiveness of the proposed method. Compared with the existing efficiency evaluation methods for dealing with data uncertainty and cost allocation methods, the proposed method shows significant superiority.</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117497"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386991","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}
Pub Date : 2026-10-01Epub Date: 2026-02-22DOI: 10.1016/j.cam.2026.117500
Nasreen Almohanna , Ali Ahmad , Khawlah Alhulwah , Ali N.A. Koam , Hamdan Alshehri
Graph theory currently encompasses the study of several subjects, ranging from algebraic features of structures to the analysis of chemical graph structures without experimental procedures. Additionally, it involves the development of networks using topological Indices (TIs). Exploring different networks and utilising TIs is an expanding field of contemporary research. The use of optoelectronic technology in optical transposition interconnection systems (OTIS) offers an effective solution to the ongoing problem of storing and sending data with comprehensive information. This is due to the reduced power requirements and broad bandwidth capabilities of optoelectronic systems, which make them well-suited for this task. The integration of radio communication and electrical technology has transformed OTIS into a highly valued network, enhancing the efficiency of existing optoelectronic computers. OTIS is characterised by the biswapped network (BN) that is formed with the help of path graph and denoted as . This research work focused on the M-polynomial and entropy measures in relation to the number of connections between nodes of the graph and its largest subgraph that preserves twin nodes ().
{"title":"Exploration of M-polynomial and entropy measures of biswapped networks with connection number approaches","authors":"Nasreen Almohanna , Ali Ahmad , Khawlah Alhulwah , Ali N.A. Koam , Hamdan Alshehri","doi":"10.1016/j.cam.2026.117500","DOIUrl":"10.1016/j.cam.2026.117500","url":null,"abstract":"<div><div>Graph theory currently encompasses the study of several subjects, ranging from algebraic features of structures to the analysis of chemical graph structures without experimental procedures. Additionally, it involves the development of networks using topological Indices (TIs). Exploring different networks and utilising TIs is an expanding field of contemporary research. The use of optoelectronic technology in optical transposition interconnection systems (OTIS) offers an effective solution to the ongoing problem of storing and sending data with comprehensive information. This is due to the reduced power requirements and broad bandwidth capabilities of optoelectronic systems, which make them well-suited for this task. The integration of radio communication and electrical technology has transformed OTIS into a highly valued network, enhancing the efficiency of existing optoelectronic computers. OTIS is characterised by the biswapped network (BN) that is formed with the help of path graph <span><math><msub><mi>P</mi><mi>m</mi></msub></math></span> and denoted as <span><math><mrow><mi>B</mi><mo>(</mo><msub><mi>P</mi><mi>m</mi></msub><mo>)</mo></mrow></math></span>. This research work focused on the M-polynomial and entropy measures in relation to the number of connections between nodes of the graph <span><math><mrow><mi>B</mi><mo>(</mo><msub><mi>P</mi><mi>m</mi></msub><mo>)</mo></mrow></math></span> and its largest subgraph that preserves twin nodes (<span><math><mrow><mi>M</mi><mo>(</mo><mi>B</mi><mrow><mo>(</mo><msub><mi>P</mi><mi>m</mi></msub><mo>)</mo></mrow><mo>)</mo></mrow></math></span>).</div></div>","PeriodicalId":50226,"journal":{"name":"Journal of Computational and Applied Mathematics","volume":"484 ","pages":"Article 117500"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386998","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}
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