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中国数学前沿
高等教育出版社,Springer
中国数学前沿

高等教育出版社,Springer

季刊

1673-3452

100029

北京市朝阳区惠新东街4号富盛大厦15层

中国数学前沿/Journal Frontiers of Mathematics in ChinaCSCDCSTPCDSCI
查看更多>>文章范围包括数学领域的综述、研究论文,涵盖基础数学、应用数学、计算数学与科学工程计算、数理统计等各学科分支。读者对象为从事数学研究与教学的科研人员、高等院校教师及在读研究生等。
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    The acyclic chromatic index of planar graphs without 4-,6-cycles and intersecting triangles

    Yuehua BUQi JIAHongguo ZHU
    117-136页
    查看更多>>摘要:A proper edge k-coloring is a mapping φ:E(G)→ {1,2,…,k} such that any two adjacent edges receive different colors.A proper edge k-coloring φ of G is called acyclic if there are no bichromatic cycles in G.The acyclic chromatic index of G,denoted by x'a(G),is the smallest integer k such that G is acyclically edge k-colorable.In this paper,we show that if G is a plane graph without 4-,6-cycles and intersecting 3-cycles,Δ(G)≥ 9,then x'a(G)≤Δ(G)+1.

    Low-rank spectral estimation algorithm of learning Markov model

    Yongye ZHAOShujun BI
    137-155页
    查看更多>>摘要:This paper proposes a low-rank spectral estimation algorithm of learning Markov model.First,an approximate projection algorithm for the rank-constrained frequency matrix set is proposed,and thereafter its local Lipschitzian error bound established.Then,we propose a low-rank spectral estimation algorithm for estimating the state transition frequency matrix and the probability matrix of Markov model by applying the approximate projection algorithm to correct the maximum likelihood estimation of the frequency matrix,and prove that there is only a multiplying constant difference in estimation errors between the low-rank spectral estimation and the maximum likelihood estimation under appropriate conditions.Finally,numerical comparisons with the prevailing maximum likelihood estimation,spectral estimation,and rank-constrained maxi-mum likelihood estimation show that the low-rank spectral estimation algorithm is effective.

    An overview of image restoration based on variational regularization

    Qibin FANYuling JIAO
    157-180页
    查看更多>>摘要:Image restoration is a complicated process in which the original information can be recovered from the degraded image model caused by lots of factors.Mathematically,image restoration problems are ill-posed inverse prob-lems.In this paper image restoration models and algorithms based on variational regularization are surveyed.First,we review and analyze the typical models for denoising,deblurring and inpainting.Second,we construct a unified restoration model based on variational regularization and summarize the typical numerical methods for the model.At last,we point out eight diffcult problems which remain open in this field.