  This paper studies a joint design problem where a seller can design both the
signal structures for the agents to learn their values, and the allocation and
payment rules for selling the item. In his seminal work, Myerson (1981) shows
how to design the optimal auction with exogenous signals. We show that the
problem becomes NP-hard when the seller also has the ability to design the
signal structures. Our main result is a polynomial-time approximation scheme
(PTAS) for computing the optimal joint design with at most an $\epsilon$
multiplicative loss in expected revenue. Moreover, we show that in our joint
design problem, the seller can significantly reduce the information rent of the
agents by providing partial information, which ensures a revenue that is at
least $1 - \frac{1}{e}$ of the optimal welfare for all valuation distributions.
