  This paper presents novel algorithms which exploit the intrinsic algebraic
and combinatorial structure of the matrix completion task for estimating
missing en- tries in the general low rank setting. For positive data, we
achieve results out- performing the state of the art nuclear norm, both in
accuracy and computational efficiency, in simulations and in the task of
predicting athletic performance from partially observed data.
