  In this paper, we consider the generalized low rank approximation of the
correlation matrices problem which arises in the asset portfolio. We first
characterize the feasible set by using the Gramian representation together with
a special trigonometric function transform, and then transform the generalized
low rank approximation of the correlation matrices problem into an
unconstrained optimization problem. Finally, we use the conjugate gradient
algorithm with the strong Wolfe line search to solve the unconstrained
optimization problem. Numerical examples show that our new method is feasible
and effective.
