Subdivision schemes of sets and the approximation of set-valued functions in the symmetric difference metric
For researchers in set-valued approximation and geometric modeling, this provides a novel framework for subdivision on sets, though the impact is domain-specific and incremental.
This work constructs subdivision schemes for general subsets of R^n in the symmetric difference metric, enabling approximation of set-valued functions. The schemes, based on a new weighted average of sets, achieve monotonicity preservation and extend to general metric spaces.
In this work we construct subdivision schemes refining general subsets of R^n and study their applications to the approximation of set-valued functions. Differently from previous works on set-valued approximation, our methods are developed and analyzed in the metric space of Lebesgue measurable sets endowed with the symmetric difference metric. The construction of the set-valued subdivision schemes is based on a new weighted average of two sets, which is defined for positive weights (corresponding to interpolation) and also when one weight is negative (corresponding to extrapolation). Using the new average with positive weights, we adapt to sets spline subdivision schemes computed by the Lane-Riesenfeld algorithm, which requires only averages of pairs of numbers. The averages of numbers are then replaced by the new averages of pairs of sets. Among other features of the resulting set-valued subdivision schemes, we prove their monotonicity preservation property. Using the new weighted average of sets with both positive and negative weights, we adapt to sets the 4-point interpolatory subdivision scheme. Finally we discuss the extension of the results obtained in the metric spaces of sets, to general metric spaces endowed with an averaging operation satisfying certain properties.