Adaptive Sampling for Fast Constrained Maximization of Submodular Function
This addresses large-scale machine learning tasks like data summarization by improving efficiency for constrained submodular optimization, though it is incremental as it builds on known methods for adaptivity and constraints.
The paper tackles the problem of maximizing non-monotone submodular functions under general side constraints, achieving a (p + O(√p))-approximation with poly-logarithmic adaptivity and polynomial queries, which provides an exponential speed-up in adaptivity over previous constant-factor approximation algorithms.
Several large-scale machine learning tasks, such as data summarization, can be approached by maximizing functions that satisfy submodularity. These optimization problems often involve complex side constraints, imposed by the underlying application. In this paper, we develop an algorithm with poly-logarithmic adaptivity for non-monotone submodular maximization under general side constraints. The adaptive complexity of a problem is the minimal number of sequential rounds required to achieve the objective. Our algorithm is suitable to maximize a non-monotone submodular function under a $p$-system side constraint, and it achieves a $(p + O(\sqrt{p}))$-approximation for this problem, after only poly-logarithmic adaptive rounds and polynomial queries to the valuation oracle function. Furthermore, our algorithm achieves a $(p + O(1))$-approximation when the given side constraint is a $p$-extendible system. This algorithm yields an exponential speed-up, with respect to the adaptivity, over any other known constant-factor approximation algorithm for this problem. It also competes with previous known results in terms of the query complexity. We perform various experiments on various real-world applications. We find that, in comparison with commonly used heuristics, our algorithm performs better on these instances.