Simplification and elimination of redundant linear arithmetic constraints
Constraint logic programming
Shadows and slices of polytopes
Proceedings of the twelfth annual symposium on Computational geometry
On computing four-finger equilibrium and force-closure grasps of polyhedral objects
International Journal of Robotics Research
Automatic discovery of linear restraints among variables of a program
POPL '78 Proceedings of the 5th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
A New Numerical Abstract Domain Based on Difference-Bound Matrices
PADO '01 Proceedings of the Second Symposium on Programs as Data Objects
Convex Optimization
Higher-Order and Symbolic Computation
The octahedron abstract domain
Science of Computer Programming
Generating All Vertices of a Polyhedron Is Hard
Discrete & Computational Geometry
Logahedra: A New Weakly Relational Domain
ATVA '09 Proceedings of the 7th International Symposium on Automated Technology for Verification and Analysis
Two variables per linear inequality as an abstract domain
LOPSTR'02 Proceedings of the 12th international conference on Logic based program synthesis and transformation
Interprocedurally analysing linear inequality relations
ESOP'07 Proceedings of the 16th European conference on Programming
Relational Abstract Domain of Weighted Hexagons
Electronic Notes in Theoretical Computer Science (ENTCS)
Generalizing the template polyhedral domain
ESOP'11/ETAPS'11 Proceedings of the 20th European conference on Programming languages and systems: part of the joint European conferences on theory and practice of software
Fast interprocedural linear two-variable equalities
ACM Transactions on Programming Languages and Systems (TOPLAS)
Efficient strongly relational polyhedral analysis
VMCAI'06 Proceedings of the 7th international conference on Verification, Model Checking, and Abstract Interpretation
Exploiting sparsity in polyhedral analysis
SAS'05 Proceedings of the 12th international conference on Static Analysis
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The abstract domain of polyhedra lies at the heart of many program analysis techniques. However, its operations can be expensive, precluding their application to polyhedra that involve many variables. This paper describes a new approach to computing polyhedral domain operations. The core of this approach is an algorithm to calculate variable elimination (projection) based on parametric linear programming. The algorithm enumerates only non-redundant inequalities of the projection space, hence permits anytime approximation of the output.