Chinese Quarterly Journal of Mathematics ›› 2023, Vol. 38 ›› Issue (2): 210-220.doi: 10.13371/j.cnki.chin.q.j.m.2023.02.008

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The Least Squares {P,Q,k+1}-Reflexive Solution to a Matrix Equation

  

  1.  School of Mathematics and Science, Hebei GEO University, Shijiazhuang, 050031, China; 
  • Received:2022-03-25 Online:2023-06-30 Published:2023-06-30
  • Contact: LI Hao-xue (1996-), female, native of Handan, Hebei, master student of Hebei GEO University, engages in matrix algebra. E-mail: lihaoxue007@hgu.edu.cn
  • About author:DONG Chang-zhou, male, native of Shijiazhuang, Hebei, associate professor of Hebei GEO University, engages in matrix algebra; LI Hao-xue (1996-), female, native of Handan, Hebei, master student of Hebei GEO University, engages in matrix algebra
  • Supported by:
     Supported by the Education Department Foundation of Hebei Province ( Grant No.
    QN2015218).

Abstract:  Let P ∈C m×m and Q∈C n×n be Hermitian and {k +1 } -potent matrices,
i.e., P k+1 = P = P ∗ , Q k+1 = Q = Q ∗ , where ( · ) ∗ stands for the conjugate transpose of a
matrix. A matrix X ∈C m×n is called {P,Q,k +1 } -reflexive (anti-reflexive) if PXQ = X
( PXQ = −X ). In this paper, the least squares solution of the matrix equation AXB = C
subject to {P,Q,k +1 } -reflexive and anti--reflexive constraints are studied by converting
into two simpler cases: k=1 and k=2.

Key words:  Matrix equations, Potent matrix, {P,Q,k +1 } -reflexive (anti-reflexive);
Canonical correlation decomposition,
Least squares solution

CLC Number: