Pareto Optimization for Subset Selection with Dynamic Cost Constraints


  • Vahid Roostapour The University of Adelaide
  • Aneta Neumann The University of Adelaide
  • Frank Neumann The University of Adelaide
  • Tobias Friedrich Hasso Plattner Institute



In this paper, we consider the subset selection problem for function f with constraint bound B which changes over time. We point out that adaptive variants of greedy approaches commonly used in the area of submodular optimization are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a φ = (αf/2)(1− α1f )-approximation, where αf is the sube modularity ratio of f, for each possible constraint bound bB. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms.




How to Cite

Roostapour, V., Neumann, A., Neumann, F., & Friedrich, T. (2019). Pareto Optimization for Subset Selection with Dynamic Cost Constraints. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 2354-2361.



AAAI Technical Track: Heuristic Search and Optimization