A fast algorithm for solving large scale mean-variance models by compact factorization of covariance matrices

A fast algorithm for solving large scale MV (mean-variance) portfolio optimization problems is proposed. It is shown that by using T independent data representing the rate of return of the assets, the MV model consisting of n assets can be put into a quadratic program with n+T variables, T linear constraints and T quadratic terms in the objective function. As a result, the computation time required to solve this problem would increase very mildly as a function of n. This implies that a very large scale MV model can now be solved in a practical amount of time.