Given a set of equations with unknowns matrixes X and Y expressed as: $$XBX^{T} +YCY^{T} =A$$ where
A is symmetric and positive definite (i.e. in my particular case it is a covariance matrix),
C and B are diagonal and positive definite (in my particular case they are the matrixes of eigenvalues of A where the first k eigenvalues are stored on the diagonal of B and the remaining are on the diagonal of C),
and the unknowns are X and Y (in my case I can put X and Y equal to the eigenvectors corresponding to the eigenvalues in B and C respectively),
the question 1 is: is the solution for X and Y unique subject to the added constraints that $1^{T}X=1$ and $1^{T}Y=1$?
This question may be rephrased as: provided that we are working with normalized eigenvectors, is it true that the equation above is solved only putting X and Y equal to the normalized eigenvectors corresponding to the eigenvalues in B and C? That is equivalent to saying: are normalized eigenvectors unique for A? Assume A is a matrix whose eigenvalues are all distinct (i.e. all the eigenvalues have multiplicity 1).
The question 2 is I have read somewhere that it is rare to find estimated covariance matrixes in real-life applications (for example we have a sample of data for random variables and estimate their variance covariance matrix) where some eigenvalues have multiplicity which is not 1. Can you confirm?
I add this link because it is helpful to the subject of uniqueness of eigenvectors for each eigenvalue with multiplicity 1.