次可分与拟可分的Banach空间

Ｘ是Ｂａｎａｃｈ空间，对Ｘ的每一弱可分且弱紧的子集Ｋ，若（ｋ，ω）可度量，则称Ｘ是次可分的。如果对任意的可分，则称Ｘ是拟可分的。本文证明了这两类新引入的空间比可分空间与弱紧生成的Ｂａｎａｃｈ空间都要广泛并且保留了可分空间的许多重要性质。     

r—不可分矩阵的本原指数

本文给出了 n阶 r—不可分矩阵的本原指数的上界 ,即 n阶 r—不可分矩阵的本原指数 ( A)≤ n-r( 1≤ r2 ,都能找到一类本原指数为 n-1的 n阶 1—不可分矩阵 .证明了 n阶 1—不可分矩阵的本原指数集 En={ 1 ,2 ,… ,wn} ( wn=n-1 ) .     

关于两粒子量子态可分性及关联性的注记

本文讨论了两粒子系统中量子态的可分性与关联性,分别得到了纯态与混合态可分的充要条件,及其元素必须满足的条件.用量子态元素之间的关系,给出了乘积态的刻画.此外,通过元素刻画了量子态的左(右)经典关联性与经典关联性.     

由可分组设计构造对称设计

本文提出了由一类可分组设计构造出对称设计的方法.注意到这类可分组设计的关联图对应着5类结合方案的关系图.本文利用该5类结合方案的商结合方案,由这类可分组设计构造对称设计,并举例说明了构造的具体过程.此外,提出了一种利用阵列由对称设计构造可分组设计的方法.在此基础上,证明了两个有对偶性质的可分组设计GDDDP(2,11;5;0,1)和GDDDP(2,16;6;0,1)不存在.     

具有特殊伸缩矩阵的三元不可分小波的构造

多元小波分析是分析和处理高维数字信号的有力工具.不可分多元小波被广泛地应用在模式识别、纹理分析和边缘检测等领域.本文给出了构造一类特殊伸缩矩阵的紧支撑三元不可分小波的算法,利用该算法得到的小波函数继承了来源于尺度函数和符号函数的对称性和消失矩性质,由于符号函数中的参数选取具有很大的自由度,因此可以根据不同的实际情况来动态地确定符号函数,从而为这类小波在信号处理方面的应用提供了便利.最后给出了相应的数值算例.     

可分半群的张量积

本文建立交换可分半群范畴中的张量积,证明其存在与唯一性,同时,建立交换半群的极大可分半群象与张量积之间的关系。最后证明交换可分半群量积的极大半格象同构于其极大半格象的张量积。     

用可分命题简化证明实数系中定理

修改了连续归纳法,利用修改后的连续归纳法证明了一个关于可分命题的重要定理,利用该定理简化证明实数系中若干定理.     

二阶完全对称张量空间中可分元素的坐标间的刘次关系

本文给出了二阶完全对称张量空间Ｖ＾（２）中可分元素的坐标间的齐次关系，特别地，这种齐次关系可以由行列式表示出来，并由此得到了Ｖ＾（２）中可分元素的一个等价条件。     

有限维张量积空间上的强可分算子

设F是一个域,a∈F~nF~m.若存在h∈F~m,k∈F~m,使得a=hk,则称a是可分的.空间F~nF~m上的线性算子A称为是强可分的,是指x∈F~nF~m,x可分Ax可分.本文证明了F~nF~n上的线性算子A是强可分的当且仅当存在F~n上的线性双射A_1与A_2,使得A=A_1A_2或A=A_1~T A_2;证明了F~nF~m(n≠m)上线性算子A是强可分的当且仅当存在F~n与F~m上的线性双射A_1与A_2,使得A=A_1A_2.最后,给出了可分算子、强可分算子和秩1保持映射之间的关系.     

一类量子态的迹距离相干度量的最优化证明方法

迹距离相干度量是基于迹范数提出的量化相干的一种度量.然而,很难给出一般量子态迹距离相干度量的表达式并且找到对应的最近非相干态.通过最优化方法给出了一类d×d量子态的迹距离相干度量,并且证明了这类量子态的最近非相干态就是由该量子态去掉所有非对角元素得到的对角矩阵.     

量子态上保凸组合最大特征值的映射北大核心CSCD

量子态即Hilbert空间上迹为1的正算子.量子态集合是一个凸集本文刻画了量子态集合上保持其凸组合的最大特征值不变的映射.     

Convergence rate of a proximal multiplier algorithm for separable convex minimization

In this paper, we analyse the convergence rate of the proximal algorithm proposed by us in the article [A proximal multiplier method for separable convex minimization. Optimization. 2016; 65:501–537], which has been proposed to solve a separable convex minimization problem. We prove that, under mild assumptions, the primal-dual sequences of the algorithm converge linearly to the optimal solution for a class of proximal distances.     

AN EXTENSION OF PREDICTOR-CORRECTOR ALGORITHM TO A CLASS OF CONVEX SEPARABLE PROGRAM

1.IntroductionIn[1]Mizuno,ToddandYepresentedapredictor-correctoralgorithmforlinearpramgrammingwhichpossessesaquadraticconvergencerateofthedualgaptozero.GuoandWul6]gaveamodificationofthisalgorithmforsolvingconvexquadraticprogramwithupperbounds.Itisshownthatthemodifiedmethodnotonlypreservesalltheoriginalmerits,butalsoreducesthedualgapbyaconstantfactorineachcorrectorstep,incontrasttotheMizuno,TOddandYe'soriginalpredictor--correctormethodwherethedualgapremainsunchanged.Thealgorithmdiscussedint…     

On the strong CE-property of convex sets

We consider a class of convex bounded subsets of a separable Banach space. This class includes all convex compact sets as well as some noncompact sets important in applications. For sets in this class, we obtain a simple criterion for the strong CE-property, i.e., the property that the convex closure of any continuous bounded function is a continuous bounded function. Some results are obtained concerning the extension of functions defined at the extreme points of a set in this class to convex or concave functions defined on the entire set with preservation of closedness and continuity. Some applications of the results in quantum information theory are considered.     

求解Nash均衡子问题的一个算法

n人有限博弈的混合策略组合（p1^*,…,pn^*)为Nash均衡,如果其中每一策略pi^*都是参与人i(i=1,2,…,n),对其它n-1个参与人策略组合（p1^*,…,pi 1^*,pi-1^*,…,pn^*)的最优反应,即存在n个概率向量p1^*,…,pn^*使得对i=1,2,…,n及任意k1维概率向量pi恒有vi(p1^*,…,pn^*…)小于vi(pi^*,…,pi-1^*,pi 1^*,…pn^*),其中vi为参与人i的支付函数,pi=(pil,…,piki)）为ki维概率向量,即满足条件,pij大于等于0,∑kij=1pij=1,ki是参与人i的策略空间中策略个数,i=1,2,…,n,由此,Nash均衡的求解可化为下列优化问题：求n个概率向量pi^*,…,pn^8,使得对i=1,2,…,n及任意ki维的概率向量pi满足maxxvi(P1^*,…,pi-1^*,pi,Pi 1^*,…,pn^*)=vi(P1^*,,…,Pn^*)。     

可分离凸优化问题的非精确平行分裂算法

针对一类可分离凸优化问题提出了一种非精确平行分裂算法.该算法充分利用了所求解问题的可分离结构,并对子问题进行非精确求解.在适当的条件下,证明了所提出的非精确平行分裂算法的全局收敛性,初步的数值实验说明了算法有效性.     

Inverse optimization for linearly constrained convex separable programming problems

In this paper, we study inverse optimization for linearly constrained convex separable programming problems that have wide applications in industrial and managerial areas. For a given feasible point of a convex separable program, the inverse optimization is to determine whether the feasible point can be made optimal by adjusting the parameter values in the problem, and when the answer is positive, find the parameter values that have the smallest adjustments. A sufficient and necessary condition is given for a feasible point to be able to become optimal by adjusting parameter values. Inverse optimization formulations are presented with ℓ₁ and ℓ₂ norms. These inverse optimization problems are either linear programming when ℓ₁ norm is used in the formulation, or convex quadratic separable programming when ℓ₂ norm is used.     

Idempotent states on Sekine quantum groups

Sekine quantum groups are a family of finite quantum groups. The main result of this article is to compute all the idempotent states on Sekine quantum groups, which completes the work of Franz and Skalski. This is achieved by solving a complicated system of equations using linear algebra and basic number theory. From this, we discover a new class of non-Haar idempotent states. The order structure of the idempotent states on Sekine quantum groups is also discussed. Finally we give a sufficient condition for the convolution powers of states on Sekine quantum group to converge.     

Convex quadratic programming with one constraint and bounded variables

In this paper we propose an iterative algorithm for solving a convex quadratic program with one equality constraint and bounded variables. At each iteration, a separable convex quadratic program with the same constraint set is solved. Two variants are analyzed: one that uses an exact line search, and the other a unit step size. Preliminary testing suggests that this approach is efficient for problems with diagonally dominant matrices. This work was supported by a research grant from the France-Quebec exchange program and also by NSERC Grant No. A8312. The first author was supported by a scholarship from Transport Canada while doing this research.