Computational & Applied Mathematics最新文献_第6页

A framework for convex-constrained monotone nonlinear equations and its special cases 凸约束单调非线性方程的框架及其特殊情况

3区数学

Computational & Applied Mathematics Pub Date : 2023-09-13 DOI: 10.1007/s40314-023-02446-z

Max L. N. Gonçalves, Tiago C. Menezes

引用次数: 0

Asymmetric image authentication algorithm using double random modulus decomposition and CGI 采用双随机模分解和CGI的非对称图像认证算法

3区数学

Computational & Applied Mathematics Pub Date : 2023-09-12 DOI: 10.1007/s40314-023-02443-2

Raman Yadav, Phool Singh

引用次数: 0

On the arithmetic-geometric spectral radius of bicyclic graphs 双环图的算术-几何谱半径

3区数学

Computational & Applied Mathematics Pub Date : 2023-09-12 DOI: 10.1007/s40314-023-02449-w

Yan Yuan

引用次数: 0

Inertial proximal point algorithm for the split common solution problem of monotone operator equations 单调算子方程分裂共解问题的惯性近点算法

3区数学

Computational & Applied Mathematics Pub Date : 2023-09-11 DOI: 10.1007/s40314-023-02441-4

Nguyen Song Ha, Truong Minh Tuyen, Phan Thi Van Huyen

引用次数: 0

Fair allocation algorithms for indivisible items under structured conflict constraints 结构化冲突约束下不可分割物品公平分配算法

3区数学

Computational & Applied Mathematics Pub Date : 2023-09-11 DOI: 10.1007/s40314-023-02437-0

Nina Chiarelli, Matjaž Krnc, Martin Milanič, Ulrich Pferschy, Joachim Schauer

{"title":"Fair allocation algorithms for indivisible items under structured conflict constraints","authors":"Nina Chiarelli, Matjaž Krnc, Martin Milanič, Ulrich Pferschy, Joachim Schauer","doi":"10.1007/s40314-023-02437-0","DOIUrl":"https://doi.org/10.1007/s40314-023-02437-0","url":null,"abstract":"Abstract We consider the fair allocation of indivisible items to several agents with additional conflict constraints. These are represented by a conflict graph where each item corresponds to a vertex of the graph and edges in the graph represent incompatible pairs of items which should not be allocated to the same agent. This setting combines the issues of P artition and I ndependent S et and can be seen as a partial coloring of the conflict graph. In the resulting optimization problem, each agent has its own valuation function for the profits of the items. We aim at maximizing the lowest total profit obtained by any of the agents. In a previous paper, this problem was shown to be strongly -hard for several well-known graph classes, e.g., bipartite graphs and their line graphs. On the other hand, it was shown that pseudo-polynomial time algorithms exist for the classes of chordal graphs, cocomparability graphs, biconvex bipartite graphs, and graphs of bounded treewidth. In this contribution, we extend this line of research by developing pseudo-polynomial time algorithms that solve the problem for the class of convex bipartite conflict graphs, graphs of bounded clique-width, and graphs of bounded tree-independence number. The algorithms are based on dynamic programming and also permit fully polynomial-time approximation schemes (FPTAS).","PeriodicalId":50649,"journal":{"name":"Computational & Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135981696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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

Retraction Note to: A fast adaptive algorithm for nonlinear inverse problems with convex penalty 带有凸惩罚的非线性逆问题的快速自适应算法

3区数学

Computational & Applied Mathematics Pub Date : 2023-09-01 DOI: 10.1007/s40314-023-02439-y

Gang Ren, Guanglan Gao

引用次数: 0

A weighted mass explicit scheme for convection-diffusion equations 对流扩散方程的加权质量显式格式

IF 2.6 3区数学

Computational & Applied Mathematics Pub Date : 2012-12-06 DOI: 10.1590/S1807-03022012000300004

V. Ruas

引用次数: 0

Numerical solution of coupled mass and energy balances during osmotic microwave dehydration 渗透微波脱水过程中耦合质量和能量平衡的数值解

IF 2.6 3区数学

Computational & Applied Mathematics Pub Date : 2012-12-06 DOI: 10.1590/S1807-03022012000300006

J. R. Arballo, L. Campañone, R. Mascheroni

{"title":"Numerical solution of coupled mass and energy balances during osmotic microwave dehydration","authors":"J. R. Arballo, L. Campañone, R. Mascheroni","doi":"10.1590/S1807-03022012000300006","DOIUrl":"https://doi.org/10.1590/S1807-03022012000300006","url":null,"abstract":"The mass and energy transfer during osmotic microwave drying (OD-MWD) process was studied theoretically by modeling and numerical simulation. With the aim to describe the transport phenomena that occurs during the combined dehydration process, the mass and energy microscopic balances were solved. An osmotic-diffusional model was used for osmotic dehydration (OD). On the other hand, the microwave drying (MWD) was modeled solving the mass and heat balances, using properties as function of temperature, moisture and soluble solids content. The obtained balances form highly coupled non-linear differential equations that were solved applying numerical methods. For osmotic dehydration, the mass balances formed coupled ordinary differential equations that were solved using the Fourth-order Runge Kutta method. In the case of microwave drying, the balances constituted partial differential equations, which were solved through Crank-Nicolson implicit finite differences method. The numerical methods were coded in Matlab 7.2 (Mathworks, Natick, MA). The developed mathematical model allows predict the temperature and moisture evolution through the combined dehydration process. Mathematical subject classification: Primary: 06B10; Secondary: 06D05.","PeriodicalId":50649,"journal":{"name":"Computational & Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2012-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82370693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 5

Numerical analysis of the nonlinear subgrid scale method 非线性子网格尺度法的数值分析

IF 2.6 3区数学

Computational & Applied Mathematics Pub Date : 2012-12-06 DOI: 10.1590/S1807-03022012000300003

I. P. Santos, R. C. Almeida, S. Malta

引用次数: 6

An overview of flexibility and generalized uncertainty in optimization 优化中的灵活性和广义不确定性概述

IF 2.6 3区数学

Computational & Applied Mathematics Pub Date : 2012-12-06 DOI: 10.1590/S1807-03022012000300008

W. Lodwick

{"title":"An overview of flexibility and generalized uncertainty in optimization","authors":"W. Lodwick","doi":"10.1590/S1807-03022012000300008","DOIUrl":"https://doi.org/10.1590/S1807-03022012000300008","url":null,"abstract":"Two new powerful mathematical languages, fuzzy set theory and possibility theory, have led to two optimization types that explicitly incorporate data whose values are not real-valued nor probabilistic: 1) flexible optimization and 2) optimization under generalized uncertainty. Our aim is to make clear what these two types are, make distinctions, and show how they can be applied. Flexible optimization arises when it is necessary to relax the meaning of the mathematical relation of belonging to a set (a constraint set in the context of optimization). The mathematical language of relaxed set belonging is fuzzy set theory. Optimization under generalized uncertainty arises when it is necessary to represent parameters of a model whose values are only known partially or incompletely. A natural mathematical language for the representation of partial or incomplete information about the value of a parameter is possibility theory. Flexible optimization, as delineated here, includes much of what has been called fuzzy optimization whereas optimization under generalized uncertainty includes what has been called possibilistic optimization. We explore why flexible optimization and optimization under generalized uncertainty are distinct and important types of optimization problems. Possibility theory in the context of optimization leads to two distinct types of optimization under generalized uncertainty, single distribution and dual distribution optimization. Dual (possibility/necessity pairs) distribution optimization is new. Mathematical subject classification: 90C70, 65G40.","PeriodicalId":50649,"journal":{"name":"Computational & Applied Mathematics","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2012-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78449527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"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: 12