Preconditioned Conjugate Gradient methods for the estimation of General Linear Models

Abstract: The use of the Preconditioned Conjugate Gradient (PCG) method for computing the Generalized Least Squares (GLS) estimator of the General Linear Model (GLM) is considered. The GLS estimator is expressed in terms of the solution of an augmented system. That system is solved by means of the PCG method using an indefinite preconditioner. The resulting method iterates a sequence Ordinary Least Squares (OLS) estimations that converges, in exact precision, to the GLS estimator within a finite number of steps. The numerical and statistical properties of the estimator computed at an intermediate step are analytically and numerically studied. This approach allows to combine direct methods, used in the OLS step, with those of iterative methods. This advantage is exploited to design PCG methods for the estimation of Constrained GLMs and of some structured multivariate GLMs. The structure of the matrices involved are exploited as much as possible, in the OLS step. The iterative method then solves for the unexploited structure. Numerical experiments shows that the proposed methods can achieve, for these structured problems, the same precision of state of the art direct methods, but in a fraction of the time.