The efficient frontier computation functions require information
about each asset in the portfolio. This data is entered into the function
via two matrices: an expected return vector and a covariance matrix.
The expected return vector contains the average expected return for
each asset in the portfolio. The covariance matrix is a square matrix representing the interrelationships
between pairs of assets. This information can be directly specified
or can be estimated from an asset return time series with the function
An alternative to using these portfolio optimization functions
is to use the Portfolio object (
for mean-variance portfolio optimization. This object supports gross
or net portfolio returns as the return proxy, the variance of portfolio
returns as the risk proxy, and a portfolio set that is any combination
of the specified constraints to form a portfolio set. For information
on the workflow when using Portfolio objects, see Portfolio Object Workflow.