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Approximation by modified Bernstein polynomials based on real parameters 基于实参数的修正Bernstein多项式近似
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2023005
Ruchi Singh Rajawat, Karunesh Singh, V. Mishra
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引用次数: 3
Deferred statistical boundedness on time scales 时间尺度上的延迟统计有界性
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2023006
Pallav Bhattarai, H. Dutta
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引用次数: 0
Single-machine scheduling with coupled task and rejection 具有耦合任务和拒绝的单机调度
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2023008
Fanyu Kong, Cuixia Miao, Yujia Huo, Jiaxin Song
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引用次数: 0
Numerical method for the solution of nonlinear boundary value Problems (ODE) 非线性边值问题(ODE)的数值解法
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2023022
S. Dutta, B. Mishra
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引用次数: 0
Role of federated learning in healthcare systems: A survey 联合学习在医疗保健系统中的作用:一项调查
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2023023
Neeta Rana, Hitesh Marwaha
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引用次数: 0
Unified fast algorithms for building concept lattices 构建概念格的统一快速算法
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2023037
Xifeng Wang, Jianqin Zhou, Sichun Yang
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引用次数: 0
Generalized Kantorovich modifications of positive linear operators 正线性算子的广义Kantorovich修正
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2021042
A. Acu, Ioan Cristian Buşcu, I. Raşa
Starting with a positive linear operator we apply the Kantorovich modification and a related modification. The resulting operators are investigated. We are interested in the eigenstructure, Voronovskaya formula, the induced generalized convexity, invariant measures and iterates. Some known results from the literature are extended.
从一个正线性算子开始,我们应用了Kantorovich修正和一个相关的修正。对得到的算子进行了研究。我们对特征结构、Voronovskaya公式、归纳广义凸性、不变测度和迭代感兴趣。推广了文献中一些已知的结果。
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引用次数: 7
Machine scheduling with job rejection and DeJong's learning effect 具有作业拒绝的机器调度与DeJong学习效应
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2022024
Jie Gao, Juan Zou, Xiaoxuan Cheng

This paper mainly discusses machine scheduling problems with job rejection and DeJong's learning effect. The goal is to determine the job sequence of the accepted jobs so as to minimize the scheduling cost of the accepted jobs plus the total rejection penalty of the rejected jobs. The scheduling costs of the accepted jobs are the makespan and the total completion time. For the single-machine setting, we show that both of the objectives can be optimally solved in polynomial time. For the parallel-machine setting, we show that minimizing the total completion time of the accepted jobs plus the total rejection penalty of the rejected jobs is still polynomially solvable, whereas the other problem is begin{document}$ NP $end{document}-hard, for which we provide a fully polynomial-time approximation scheme (FPTAS).

This paper mainly discusses machine scheduling problems with job rejection and DeJong's learning effect. The goal is to determine the job sequence of the accepted jobs so as to minimize the scheduling cost of the accepted jobs plus the total rejection penalty of the rejected jobs. The scheduling costs of the accepted jobs are the makespan and the total completion time. For the single-machine setting, we show that both of the objectives can be optimally solved in polynomial time. For the parallel-machine setting, we show that minimizing the total completion time of the accepted jobs plus the total rejection penalty of the rejected jobs is still polynomially solvable, whereas the other problem is begin{document}$ NP $end{document}-hard, for which we provide a fully polynomial-time approximation scheme (FPTAS).
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引用次数: 1
Error analysis of kernel regularized pairwise learning with a strongly convex loss 具有强凸损失的核正则化成对学习的误差分析
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2022030
Shuhua Wang, B. Sheng
This paper presents a detailed performance analysis for the kernel-based regularized pairwise learning model associated with a strongly convex loss. The robustness for the model is analyzed by applying an improved convex analysis method. The results show that the regularized pairwise learning model has better qualitatively robustness according to the probability measure. Some new comparison inequalities are provided, with which the convergence rates are derived. In particular an explicit learning rate is obtained in case that the loss is the least square loss.
本文详细分析了带有强凸损失的基于核的正则化成对学习模型的性能。采用改进的凸分析方法对模型进行鲁棒性分析。结果表明,基于概率度量的正则化两两学习模型具有较好的定性鲁棒性。给出了一些新的比较不等式,并由此导出了收敛速度。特别地,在损失为最小二乘损失的情况下,得到了显式学习率。
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引用次数: 3
Almost sure convergence for the maxima and minima of strongly dependent nonstationary multivariate Gaussian sequences 强相关非平稳多元高斯序列的极大值和极小值的几乎肯定收敛性
Q3 COMPUTER SCIENCE, THEORY & METHODS Pub Date : 2023-01-01 DOI: 10.3934/mfc.2022044
Zhicheng Chen, Hongyun Zhang, Xinsheng Liu
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引用次数: 0
期刊
Mathematical foundations of computing
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