Rounded Dynamic Programming for Tree-Structured Stochastic Network Design


  • Xiaojian Wu University of Massachusetts Amherst
  • Daniel Sheldon University of Massachusetts Amherst
  • Shlomo Zilberstein University of Massachusetts Amherst



We develop a fast approximation algorithm called rounded dynamic programming (RDP) for stochastic network design problems on directed trees. The underlying model describes phenomena that spread away from the root of a tree, for example, the spread of influence in a hierarchical organization or fish in a river network. Actions can be taken to intervene in the network—for some cost—to increase the probability of propagation along an edge. Our algorithm selects a set of actions to maximize the overall spread in the network under a limited budget. We prove that the algorithm is a fully polynomial-time approximation scheme (FPTAS), that is, it finds (1−ε)-optimal solutions in time polynomial in the input size and 1/ε. We apply the algorithm to the problem of allocating funds efficiently to remove barriers in a river network so fish can reach greater portions of their native range. Our experiments show that the algorithm is able to produce near-optimal solutions much faster than an existing technique.




How to Cite

Wu, X., Sheldon, D., & Zilberstein, S. (2014). Rounded Dynamic Programming for Tree-Structured Stochastic Network Design. Proceedings of the AAAI Conference on Artificial Intelligence, 28(1).



Computational Sustainability and Artificial Intelligence