IBM个人电脑事业部宣布，将于本月18日推出最新的高性能台式机——ThinkCentre A51p。它兼具先进的图形性能和增强的处理功能——这非常适用于那些需要同时运转多个应用程序而不造成PC超负荷的中小企业用户。IBM近期还同时推出了最新的...IBM个人电脑事业部宣布，将于本月18日推出最新的高性能台式机——ThinkCentre A51p。它兼具先进的图形性能和增强的处理功能——这非常适用于那些需要同时运转多个应用程序而不造成PC超负荷的中小企业用户。IBM近期还同时推出了最新的高性价比台式机Thirlk Centre A50．展开更多
Using the axiomatic method, abstract concepts such as abstract mean, abstract convex function and abstract majorization are proposed. They are the generalizations of concepts of mean, convex function and majorization,...Using the axiomatic method, abstract concepts such as abstract mean, abstract convex function and abstract majorization are proposed. They are the generalizations of concepts of mean, convex function and majorization, respectively. Through the logical deduction, the fundamental theorems about abstract majorization inequalities are established as follows: for arbitrary abstract mean Σ and $ \Sigma ' $ and abstract ∑ ? $ \Sigma ' $ strict convex function f(x) on the interval I, if x i , y i ∈ I (i = 1, 2,..., n) satisfy that $ (x_1 ,x_2 , \ldots ,x_n ) \prec _n^\Sigma (y_1 ,y_2 , \ldots ,y_n ) $ then $ \Sigma ' $ {f(x 1), f(x 2),..., f(x n )} ? $ \Sigma ' $ {f(y 1), f(y 2),..., f(y n )}. This class of inequalities extends and generalizes the fundamental theorem of majorization inequalities. Moreover, concepts such as abstract vector mean are proposed, the fundamental theorems about abstract majorization inequalities are generalized to n-dimensional vector space. The fundamental theorem of majorization inequalities about the abstract vector mean are established as follows: for arbitrary symmetrical convex set $ \mathcal{S} \subset \mathbb{R}^n $ , and n-variable abstract symmetrical $ \overline \Sigma $ ? $ \Sigma ' $ strict convex function $ \phi (\bar x) $ on $ \mathcal{S} $ , if $ \bar x,\bar y \in \mathcal{S} $ , satisfy $ \bar x \prec _n^\Sigma \bar y $ , then $ \phi (\bar x) \geqslant \phi (\bar y) $ ; if vector group $ \bar x_i ,\bar y_i \in \mathcal{S}(i = 1,2, \ldots ,m) $ satisfy $ \{ \bar x_1 ,\bar x_2 , \ldots ,\bar x_m \} \prec _n^{\bar \Sigma } \{ \bar y_1 ,\bar y_2 , \ldots ,\bar y_m \} $ , then $ \Sigma '\{ \phi (\bar x_1 ),\phi (\bar x_2 ), \ldots ,\phi (\bar x_m )\} \geqslant \Sigma '\{ \phi (\bar y_1 ),\phi (\bar y_2 ), \ldots ,\phi (\bar y_m )\} $ .展开更多
基金supported by the National Key Basic Research Project of China (Grant No. 2004CB318003)the Foundation of the Education Department of Sichuan Province of China (Grant No. 07ZA087)
文摘Using the axiomatic method, abstract concepts such as abstract mean, abstract convex function and abstract majorization are proposed. They are the generalizations of concepts of mean, convex function and majorization, respectively. Through the logical deduction, the fundamental theorems about abstract majorization inequalities are established as follows: for arbitrary abstract mean Σ and $ \Sigma ' $ and abstract ∑ ? $ \Sigma ' $ strict convex function f(x) on the interval I, if x i , y i ∈ I (i = 1, 2,..., n) satisfy that $ (x_1 ,x_2 , \ldots ,x_n ) \prec _n^\Sigma (y_1 ,y_2 , \ldots ,y_n ) $ then $ \Sigma ' $ {f(x 1), f(x 2),..., f(x n )} ? $ \Sigma ' $ {f(y 1), f(y 2),..., f(y n )}. This class of inequalities extends and generalizes the fundamental theorem of majorization inequalities. Moreover, concepts such as abstract vector mean are proposed, the fundamental theorems about abstract majorization inequalities are generalized to n-dimensional vector space. The fundamental theorem of majorization inequalities about the abstract vector mean are established as follows: for arbitrary symmetrical convex set $ \mathcal{S} \subset \mathbb{R}^n $ , and n-variable abstract symmetrical $ \overline \Sigma $ ? $ \Sigma ' $ strict convex function $ \phi (\bar x) $ on $ \mathcal{S} $ , if $ \bar x,\bar y \in \mathcal{S} $ , satisfy $ \bar x \prec _n^\Sigma \bar y $ , then $ \phi (\bar x) \geqslant \phi (\bar y) $ ; if vector group $ \bar x_i ,\bar y_i \in \mathcal{S}(i = 1,2, \ldots ,m) $ satisfy $ \{ \bar x_1 ,\bar x_2 , \ldots ,\bar x_m \} \prec _n^{\bar \Sigma } \{ \bar y_1 ,\bar y_2 , \ldots ,\bar y_m \} $ , then $ \Sigma '\{ \phi (\bar x_1 ),\phi (\bar x_2 ), \ldots ,\phi (\bar x_m )\} \geqslant \Sigma '\{ \phi (\bar y_1 ),\phi (\bar y_2 ), \ldots ,\phi (\bar y_m )\} $ .
