Near optimality of greedy strategies for string submodular functions with forward and backward curvature constraints
Zhenliang Zhang, Zengfu Wang, Edwin K. P. Chong, Ali Pezeshki, William Moran · 2013
The problem of choosing a string of actions to optimize an objective function that is string submodular has been considered in [1]. There it is shown that the greedy strategy, consisting of a string of actions that only locally maximizes the step-wise gain in the objective function, achieves at least a (1-e-1)-approximation to the optimal strategy. This paper improves this approximation by introducing additional constraints on curvature, namely, total backward curvature, total forward curvature, and elemental forward curvature. We show that if the objective function has total backward curvature σ, then the greedy strategy achieves at least a 1/σ(1 - e-σ)-approximation of the optimal strategy. If the objective function has total forward curvature c, then the greedy strategy achieves at least a (1 - ε)-approximation of the optimal strategy. Moreover, we consider a generalization of the diminishing-return property by defining the elemental forward curvature. We also introduce the notion of string-matroid and consider the problem of maximizing the objective function subject to a string-matroid constraint.