Numerical Invariants through Convex Relaxation and Max-Strategy Iteration

Details Numerical Invariants through Convex Relaxation and Max-Strategy Iteration Numerical Invariants through Convex Relaxation and Max-Strategy Iteration

2012-04-05

arxiv.org/abs/1204.1147v1

Computer Science

Programming Languages

42 pages, conference version appears in the proceedings of the Static Analysis Symposium 2010

Scientific paper

In this article we develop a max-strategy improvement algorithm for computing least fixpoints of operators on on the reals that are point-wise maxima of finitely many monotone and order-concave operators. Computing the uniquely determined least fixpoint of such operators is a problem that occurs frequently in the context of numerical program/systems verification/analysis. As an example for an application we discuss how our algorithm can be applied to compute numerical invariants of programs by abstract interpretation based on quadratic templates.

Affiliated with

Also associated with

No associations

Rating

Numerical Invariants through Convex Relaxation and Max-Strategy Iteration does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Numerical Invariants through Convex Relaxation and Max-Strategy Iteration, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Numerical Invariants through Convex Relaxation and Max-Strategy Iteration will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-212362