Minimization Problem for Symmetric Orthogonal Anti-Symmetric Matrices

Abstract

By applying the generalized singular value decomposition and the canonical correlation decomposition simultaneously, we derive an analytical expression of the optimal approximate solution $\widehat X$, which is both a least-squares symmetric orthogonal anti-symmetric solution of the matrix equation $A^TXA=B$ and a best approximation to a given matrix $X^*$. Moreover, a numerical algorithm for finding this optimal approximate solution is described in detail, and a numerical example is presented to show the validity of our algorithm.