  In the present paper, we propose and analyze a novel method for estimating a
univariate regression function of bounded variation. The underpinning idea is
to combine two classical tools in nonparametric statistics, namely isotonic
regression and the estimation of additive models. A geometrical interpretation
enables us to link this iterative method with Von Neumann's algorithm.
Moreover, making a connection with the general property of isotonicity of
projection onto convex cones, we derive another equivalent algorithm and go
further in the analysis. As iterating the algorithm leads to overfitting,
several practical stopping criteria are also presented and discussed.
