Interval analysis yields efficient dynamic filters for computational geometry

geometric predicates and describe an efficient implementation of interval
arithmetic that is strongly influenced by the rounding modes of the
widely used IEEE 754 standard. Using this approach we design an efficient
floating point filter for the computation of the sign of a determinant
that works for arbitrary dimensions. Furthermore we show how to use
our interval techniques for exact linear optimization problems of low
dimension as they arise in geometric computing. We validate our approach
experimentally, comparing it with other static, dynamic and semi-static
filters.