期刊名称：CORE Discussion Papers / Center for Operations Research and Econometrics (UCL), Louvain
出版年度：2010
卷号：2010
期号：1
出版社：Center for Operations Research and Econometrics (UCL), Louvain
摘要：The nonnegative rank of a nonnegative matrix is the minimum number of nonnegative rank-one
factors needed to reconstruct it exactly. The problem of determining this rank and computing the
corresponding nonnegative factors is difficult; however it has many potential applications, e.g., in
data mining, graph theory and computational geometry. In particular, it can be used to
characterize the minimal size of any extended reformulation of a given combinatorial
optimization program. In this paper, we introduce and study a related quantity, called the
restricted nonnegative rank. We show that computing this quantity is equivalent to a problem in
polyhedral combinatorics, and fully characterize its computational complexity. This in turn sheds
new light on the nonnegative rank problem, and in particular allows us to provide new improved
lower bounds based on its geometric interpretation. We apply these results to slack matrices and
linear Euclidean distance matrices and obtain counter-examples to two conjectures of Beasly and
Laffey, namely we show that the nonnegative rank of linear Euclidean distance matrices is not
necessarily equal to their dimension, and that the rank of a matrix is not always greater than the
nonnegative rank of its square.
