applying a classical joint diagonalization algorithm to two wavelet variance matrices of the mixed process

solution. By allowing the random walk to transition based on multiple matrices, we decrease the variance of

models of inverse covariance matrices, filtered covariance matrices, portfolio optimization that

of the regression function and a method to combine standardized non-zero central matrices. Another

variance-covariance matrices. We also conclude that the pEL function tends to be much smoother, in settings

input matrices. Our SDP relies on a semidefinite relaxation of the set of normalized clustering matrices

the number of common rows and common columns for pairs of matrices achieving this value. We discover