Polyhedron over-approximation for complexity reduction in static analysis
Yassamine Seladji, Zheng Qu
Abstract
Yassamine Seladji, Zheng Qu
Abstract
Polyhedra are widely used in the verification of numerical programs. Specially, in the field of static analysis by abstract interpretation to express the program invariants. Polyhedra make the analysis very expressive but also very time consuming. That cost is mostly due to the minimization function, which is used to maintain polyhedra in their minimal representation without redundant constraints or generators. In this article, we propose method to over-approximate a polyhedron by minimizing the loss of accuracy. The idea is to find a good trade off between accuracy and execution time. The proposed method is applied as an alternative to the minimization function for the template polyhedra abstract domain.
OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
Polyhedra are widely used in the verification of numerical programs. Specially, in the field of static analysis by abstract interpretation to express the program invariants. Polyhedra make the analysis very expressive but also very time consuming. That cost is mostly due to the minimization function, which is used to maintain polyhedra in their minimal representation without redundant constraints or generators. In this article, we propose method to over-approximate a polyhedron by minimizing the loss of accuracy. The idea is to find a good trade off between accuracy and execution time. The proposed method is applied as an alternative to the minimization function for the template polyhedra abstract domain.
Key concepts: Polyhedron, Abstract interpretation, Reduction (mathematics), Computer science, Function (biology), Minification, Representation (politics), Algorithm