I have specified a problem whose objetive function comprises a norm error cost function to be minimized subject to LMI constraints.
minimize norm (error)
subject to LMI ==semidefinite
CVX does solve the problem with an excelent fit to the data but there seems to be some very small negative (something of the magnitude -1e^-7) eigenvalues which violate the constraints.
Is there a way to require cvx not to allow for negative eigenvalues? So to speak could I raise the tolerance to some small but positive eigenvalue, say +1e^-7?