Least-squares solution of AXB = D with respect to symmetric positive semidefinite matrix X is considered. By making use of the generalized singular value decomposition, we derive general analytic formulas, and present...Least-squares solution of AXB = D with respect to symmetric positive semidefinite matrix X is considered. By making use of the generalized singular value decomposition, we derive general analytic formulas, and present necessary and sufficient conditions for guaranteeing the existence of the solution. By applying MATLAB 5.2, we give some numerical examples to show the feasibility and accuracy of this construction technique in the finite precision arithmetic.展开更多
本文研究如下实对称矩阵广义特征值反问题: 问题IGEP,给定X∈R^(n×m),1=diag(λ_II_k_I,…,λ_pI_k_p)∈R^(n×m),并且λ_I,…,λ_p互异,sum from i=1 to p(k_i=m,求K,M∈SR^(n×n),或K∈SR^(n×n),M∈SR_0^(n×m)...本文研究如下实对称矩阵广义特征值反问题: 问题IGEP,给定X∈R^(n×m),1=diag(λ_II_k_I,…,λ_pI_k_p)∈R^(n×m),并且λ_I,…,λ_p互异,sum from i=1 to p(k_i=m,求K,M∈SR^(n×n),或K∈SR^(n×n),M∈SR_0^(n×m),或K,M∈SR_0^(n×n),或K∈SR^(n×n),M∈SR_+^(n×n),或K∈SR_0^(n×n),M∈SR_+^(n×n),或K,M∈SR_+^(n×m), (Ⅰ)使得 KX=MXA, (Ⅱ)使得 X^TMX=I_m,KX=MXA,其中SR^(n×n)={A∈R^(n×n)|A^T=A},SR_0^(n×n)={A∈SR^(n×n)|X^TAX≥0,X∈R^n},SR_+^(n×n)={A∈SR^(n×n)|X^TAX>0,X∈R^n,X≠0}. 利用矩阵X的奇异值分解和正交三角分解,我们给出了上述问题的解的表达式.展开更多
基金Subsidized by The Special Funds For Major State Basic Research Project G1999032803.
文摘Least-squares solution of AXB = D with respect to symmetric positive semidefinite matrix X is considered. By making use of the generalized singular value decomposition, we derive general analytic formulas, and present necessary and sufficient conditions for guaranteeing the existence of the solution. By applying MATLAB 5.2, we give some numerical examples to show the feasibility and accuracy of this construction technique in the finite precision arithmetic.
