  We present an algorithm to approximate the solutions to variational problems
where set of admissible functions consists of convex functions. The main
motivator behind this numerical method is estimating solutions to Adverse
Selection problems within a Principal-Agent framework. Problems such as product
lines design, optimal taxation, structured derivatives design, etc. can be
studied through the scope of these models. We develop a method to estimate
their optimal pricing schedules.
