An Algorithm for $L_{\mathrm{\infty }}$ Approximation by Step Functions

An algorithm is given for determining an optimal $b$-step approximation of weighted data, where the error is measured with respect to the $L_{\mathrm{\infty }}$ norm. For data presorted by the independent variable the algorithm takes $\Theta(n + \log n \cdot b(1+\log n/b))$timeand$\Theta(n)$space\mathrm{.}Thisis$\Theta(n \log n)$intheworstcaseand$\Theta(n)$when$b = O(n/\log n \log\log n)$. A minor change determines an optimal reduced isotonic regression in the same time and space bounds, and the algorithm also solves the $k$-center problem for 1-dimensional weighted data.