In discrete optimization, representing an objective function as an s-t cut function of a network is a basic technique to design an efficient minimization algorithm. A network representable function can be minimized by computing a minimum s-t cut of a directed network, which is a very easy and fastly solved problem. Hence it is natural to ask what functions are network representable. In the case of pseudo Boolean functions (functions on {0, 1}n), it is known that any submodular function on {0, 1}3 is network representable. Živný-Cohen-Jeavons showed by using the theory of expressive power that a certain submodular function on {0, 1}4 is not network representable. In this paper, we introduce a general framework for the network representability of functions on Dn, where D is an arbitrary finite set. We completely characterize network representable functions on {0, 1}n in our new definition. We can apply the expressive power theory to the network representability in the proposed definition. We prove that some ternary bisubmodular function and some binary k-submodular function are not network representable.
CITATION STYLE
Iwamasa, Y. (2016). On a general framework for network representability in discrete optimization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9849 LNCS, pp. 369–380). Springer Verlag. https://doi.org/10.1007/978-3-319-45587-7_32
Mendeley helps you to discover research relevant for your work.