We study the problem of maximizing a monotone submodular function subject to a matroid
constraint, and present for it a deterministic non-oblivious local search algorithm that has an …

For the problem of maximizing a monotone, submodular function with respect to a cardinality
constraint $ k $ on a ground set of size $ n $, we provide an algorithm that achieves the state …

For constrained, not necessarily monotone submodular maximization, guiding the measured
continuous greedy algorithm with a local search algorithm currently obtains the state-of-the …

This paper proposes two fast streaming algorithms for the problem of k-submodular
maximization over the ground set of n elements under the knapsack constraint which is …

In this paper, we provide the first deterministic algorithm that achieves $1/2$-approximation
for monotone submodular maximization subject to a knapsack constraint, while making a …

Non-monotone constrained submodular maximization plays a crucial role in various
machine learning applications. However, existing algorithms often struggle with a trade-off …

We study combinatorial, parallelizable algorithms for maximization of a submodular function,
not necessarily monotone, with respect to a cardinality constraint k. We improve the best …

Data summarization has become a valuable tool in understanding even terabytes of data.
Due to their compelling theoretical properties, submodular functions have been the focus of …

We investigate the problem of k-submodular maximization under a knapsack constraint over
the ground set of size n. This problem finds many applications in various fields, such as multi …

Submodular function maximization has been studied extensively in recent years due to its
numerous applications in machine learning and artificial intelligence. We study a natural …