Learning with Submodular Functions: A Convex Optimization Perspective - Foundations and Trends (R) in Machine Learning

Presents the theory of submodular functions in a self-contained way from a convex analysis perspective, presenting tight links between certain polyhedra, combinatorial optimization and convex optimization problems. In particular, it describes how submodular function minimization is equivalent to solving a variety of convex optimization problems.