  The efficiency of modern optimization methods, coupled with increasing
computational resources, has led to the possibility of real-time optimization
algorithms acting in safety critical roles. There is a considerable body of
mathematical proofs on on-line optimization programs which can be leveraged to
assist in the development and verification of their implementation. In this
paper, we demonstrate how theoretical proofs of real-time optimization
algorithms can be used to describe functional properties at the level of the
code, thereby making it accessible for the formal methods community. The
running example used in this paper is a generic semi-definite programming (SDP)
solver. Semi-definite programs can encode a wide variety of optimization
problems and can be solved in polynomial time at a given accuracy. We describe
a top-to-down approach that transforms a high-level analysis of the algorithm
into useful code annotations. We formulate some general remarks about how such
a task can be incorporated into a convex programming autocoder. We then take a
first step towards the automatic verification of the optimization program by
identifying key issues to be adressed in future work.
