【24h】

On Perturbation bounds for HR decomposition

机译:关于HR分解的摄动界

获取原文

摘要

In [1], Bunser-Gerstner showed that almost every complex square matrix A cart be decomposed into a product of a so-called pseudo-Hermitian matrix H and an upper triangular matrix:A=HR and gave the necessary conditions for the existence and the uniqueness of HR decomposition. Let J = diag(±1 ) be a signature matrix. Let A be the set of those matrices A for which the leading principal minors of A* J A have the same signs as the corresponding minors of J, R. Bhatia in [2] showed that for any A∈A has a decomposition A= HR,where R is the upper triangular matrices whose diagonal entries are all positive, H satisfies H*JH = J.Let A and A = A + E have the decomposition A = HR and A=HR, R. Bhatia [2] obtained ‖(H)-H‖F≤√(2cond(H)‖R-1 ‖‖(A)-A‖F, (1)‖( R )-R‖F≤(2)cond ( R ) ‖H-1‖‖(A)-A‖F. (2)Where cond(X) =‖1X‖‖X-1‖,‖X‖is the spectral norm, ‖X‖F is the Frobenius norm. Adopting the technique used in [3,4], in this paper we first establish some new first-order bounds of the HR decomposition for A +tG = H(t)R(t), [t]≤ε if ε is small enough that the leading principal minors of (A + tG)T J(A + tG) have the same signs as the corresponding minors of J, and get H(O) = GR-1- H R(O)R-1, (3)R(0) = Jup(R-1 GT JH + Hr jGR-1)R. (4)In particular, when △A = εG, A +△A has the decomposition A +△A = (H + △H)(R + △R) with △H = εH(0) + O(ε2), △R =εk(0) + 0(ε2).Using the row scaling on the perturbation analysis for the sensitivity of R , the more tighter bound than(2) we obtain is‖R(O)‖F/‖R‖F≤KR(A) ‖G‖F/‖H‖F (5)where KR(A)≡inf D∈Dn KR(A,D)≡inf D∈Dn 1+ξ2 K2(D-1 R) ,D =diag(δ1,δ2,…,δn) ~ l) the set of all n×n real positive definitive diagonal matrices and δD - max δj/δi,δi> 0. And ‖H(0)‖F/‖H‖F ≤KH(A)‖G‖F/‖H‖F (6)is also more tighter than (1) for the sensitivity of H. Where K H(A) ≡inf KH(A,D)≡inf 1+δD2 K2(HD-1) ‖R-1‖‖2‖G‖2.The condition estimates for the HR decomposition can also be derived by (5) and (6).
机译:在[1]中,Bunser-Gerstner表明,几乎每个复杂的方阵A都可以分解为所谓的伪Hermitian矩阵H和上三角矩阵:A = HR的乘积,并给出了存在和存在的必要条件。人力资源分解的独特性。令J = diag(±1)为特征矩阵。令A为矩阵A的集合,其中A * JA的前导主要次要符号与J,R的相应次要符号具有相同的符号。[2]中的Bhatia表明,对于任何A∈A都有分解A = HR ,其中R是对角线入口均为正的上三角矩阵,H满足H * JH =J。令A和A = A + E分解为A = HR和A = HR,R。Bhatia [2]得到了‖ (H)-H′F≤√(2cond(H)′R-1‖′(A)-A′F,(1)′(R)-R′F≤(2)cond(R)′H- 1′′(A)-A′F。(2)其中cond(X)=′1X′′X-1′,“ X′”是谱范数,“ X′F”是Frobenius范数。在[3,4]中,本文首先为A + tG = H(t)R(t)建立了HR分解的一些新的一阶界,如果ε足够小以至于导联,则[t]≤ε (A + tG)TJ(A + tG)的主要未成年人与J的相应未成年人具有相同的符号,并且得到H(O)= GR-1- HR(O)R-1,(3)R(0 )= Jup(R-1 GT JH + Hr jGR-1)R.(4)特别地,当△A =εG时,A +△A分解A +△A =(H +△H)(R +△R),其中△H =εH(0)+ O(ε2),△R =εk(0)+ 0(ε2)。对R的灵敏度进行摄动分析,比(2)得到的约束更严格的约束是R(O)F /RRF≤KR(A)GF / HF(5)其中KR (A)≡infD∈DnKR(A,D)≡infD∈Dn1 +ξ2K2(D-1 R),D = diag(δ1,δ2,...,δn)〜l)所有n的集合×n实数正定对角矩阵,且δD-maxδj/δi,δi>0。而‖H(0)‖F/‖H‖F≤KH(A)‖G‖F/‖H‖F(6)为对于H的灵敏度也比(1)更严格。其中KH(A)≡infKH(A,D)≡inf1 +δD2K2(HD-1)``R-1''''2''G''2。 HR分解的条件估计也可以由(5)和(6)得出。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号