Maximizing submodular functions under submodular constraints
Proceedings of the Thirty-Ninth Conference on Uncertainty in Artificial Intelligence, PMLR 216:1618-1627, 2023.
We consider the problem of maximizing submodular functions under submodular constraints by formulating the problem in two ways: SCSKC and DiffC. Given two submodular functions f and g where f is monotone, the objective of SCSKC problem is to find a set S of size at most k that maximizes f(S) under the constraint that g(S) < theta, for a given value of theta. The problem of DiffC focuses on finding a set S of size at most k such that h(S) = f(S)-g(S) is maximized. It is known that these problems are highly inapproximable and do not admit any constant factor multiplicative approximation algorithms unless NP is easy. Known approximation algorithms involve data-dependent approximation factors that are not efficiently computable. We initiate a study of the design of approximation algorithms where the approximation factors are efficiently computable. For the problem of SCSKC, we prove that the greedy algorithm produces a solution whose value is at least (1-1/e)f(OPT) - A, where A is the data-dependent additive error. For the DiffC problem, we design an algorithm that uses the SCSKC greedy algorithm as a subroutine. This algorithm produces a solution whose value is at least (1-1/e)h(OPT)-B, where B is also a data-dependent additive error. A salient feature of our approach is that the additive error terms can be computed efficiently, thus enabling us to ascertain the quality of the solutions produced.