On greedy heuristics for computing D-efficient saturated subsets

Abstract

D-efficient saturated subsets are natural initial solutions of various algorithms applied in statistics and computational geometry. We propose two greedy heuristics for the construction of D-efficient saturated subsets: an improvement of the method suggested by Galil and Kiefer in the context of D-optimal experimental designs and a modification of the Kumar–Yildirim method for the initiation of the minimum-volume enclosing ellipsoid algorithms. We provide mathematical insights into the two methods and compare them to the commonly used random and regularized heuristics.