2014/03/07 by Timothy C. Wang, Wang, Timothy, Romain Jobredeaux +9
Computer Science · #Embedded Systems Design Techniques #FOS: Electrical engineering #Formal Methods in Verification #Machine Learning and Algorithms #Systems and Control (eess.SY) #electronic engineering #information engineering
paper · doi:10.48550/arxiv.1403.1861
openalex publication_date 2014/03/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.