A. Simon and A. King. Exploiting Sparsity in Polyhedral Analysis. In C. Hankin and I. Siveroni, editors, Static Analysis Symposium, volume 3672 of LNCS, pages 336--351, London, UK, September 2005. Springer.

The intrinsic cost of polyhedra has lead to research on more tractable sub-classes of linear inequalities. Rather than committing to the precision of such a sub-class, this paper presents a projection algorithm that works directly on any sparse system of inequalities and which sacrifices precision only when necessary. The algorithm is based on a novel combination of the Fourier-Motzkin algorithm (for exact projection) and Simplex (for approximate projection). By reformulating the convex hull operation in terms of projection, conversion to the frame representation is avoided altogether. Experimental results conducted on logic programs demonstrate that the resulting analysis is efficient and precise.

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