简介:系统地研究了全平面收敛的B-值随机Difichlet级数的增长性,得到了在一定条件下,B-值随机Dirichlet级数在收敛平面上的增长(下)级几乎处处等于某Dirichlet级数的增长(下)级还得到了它们与指数和系数的关系式.
简介:Uponusingthedenotativetheoremofanti-HermitiangeneralizedHamiltonianmatrices,wesolveeffectivelytheleast-squaresproblemmin‖AX-B‖overanti-HermitiangeneralizedHamiltonianmatrices.WederivesomenecessaryandsufficientconditionsforsolvabilityoftheproblemandanexpressionforgeneralsolutionofthematrixequationAX=B.Inaddition,wealsoobtaintheexpressionforthesolutionofarelevantoptimalapproximateproblem.
简介:在这份报纸,我们在公制的Lp(R)(1p)得到平均-B宽度和Sobolev班Brp(R)的无限的维的-G宽度的准确价值并且获得准确(N)和强壮的asymptotic(>1)在公制的Lq的Sobolev牛肉熏香肠班Wrpq(R)的无限的维的-G宽度的结果在公制的Lqp(R)(1qp)的(R)和它的双盒子Wrp(R)。
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简介:Arealn×nsymmetricmatrixX=(x_(ij))_(n×n)iscalledabisymmetricmatrixifx_(ij)=x_(n+1-j,n+1-i).Basedontheprojectiontheorem,thecanonicalcorrelationde-compositionandthegeneralizedsingularvaluedecomposition,amethodusefulforfindingtheleast-squaressolutionsofthematrixequationA~TXA=Boverbisymmetricmatricesisproposed.Theexpressionoftheleast-squaressolutionsisgiven.Moreover,inthecorrespondingsolutionset,theoptimalapproximatesolutiontoagivenmatrixisalsoderived.Anumericalalgorithmforfindingtheoptimalapproximatesolutionisalsodescribed.
简介:Inthispaper,basedontheimplicitRunge-Kutta(IRK)methods,wederiveaclassofparallelschemethatcanbeimplementedontheparallelcomputerswithNs(Nisapositiveevennumber)processorsefficiently,anddiscusstheiterativelyB-convergenceoftheNewtoniterativeprocessforsolvingthealgebraicequationsofthescheme,secondlywepresentastrategyprovidinginitialvaluesparallellyfortheiterativeprocess.Finally,somenumericalresultsshowthatourparallelschemeishigherefficientasNisnotsolarge.
简介:§1.IntroductionLetEbeanon-zerocomplexBanaohspace,&(JET)theBanaohalgebraofalltheoperatorsfromEintoitself.Operatormeans'boundedlinearoperator'throughout.Anoperatorfunction/fromadomain(openset)Dinthecomplexplaneto@l(T£}issaidtobeanalytic,ifforanyx^Eandq>£E*(thedualspaceofE\9>(/(z)aOisanalyticintheclassical’senseinD([1],p.92).Wedenotebyja/B(D)thesetofalltheanalyticoperatorfunctionsfromDinto