An Algorithm for L∞ Approximation by Step Functions

We give an algorithm for determining an optimal step function approximation of weighted data, where the error is measured with respect to the L∞ norm. The algorithm takes Θ(n+ log n · b(1 + log n/b)) time and Θ(n) space, where b is the number of steps. Thus the time is Θ(n log n) in the worst case and Θ(n) when b = O(n/ log n log log n). A minor change determines the optimal reduced isotonic regression in the same time and space bounds, and the algorithm also solves the k-center problem for 1-dimensional data.