It is well-known that the median of an even number of datapoints is not unique; by any of many equivalent definitions, any point in the interval between the innermost points qualify. Recalling that the mean can be defined by a least squares approximation to the dataset, the median via least absolute differences, we consider minimizing the L_(p) norm from the dataset to the diagonal, and compute its limit as p approaches 1 from the right side --- the result is not the midpoint as typically used. We also construct a different family of strictly convex norms converging to L_(1) exhibiting a different limit-median

Contact details are reproduced from the original publication and may be historical.

Jeffrey Tsang

Dept. of Mathematics and Statistics, University of Guelph, 50 Stone Road East, Guelph N1G 2W1, Canada

jtsang02@uoguelph.ca

Rajesh Pereira

Dept. of Mathematics and Statistics, University of Guelph, 50 Stone Road East, Guelph N1G 2W1, Canada

pereirar@uoguelph.ca

J. Tsang, R. Pereira. “Defining a Unique Median via Minimizing Families of Norms.” Journal of Convex Analysis 24 (2017), No. 1, 199–212.