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 …

Submodular optimization has been identified as a powerful tool for many data mining
applications, where a representative subset of moderate size needs to be extracted from a …

Recent progress in (semi-) streaming algorithms for monotone submodular function
maximization has led to tight results for a simple cardinality constraint. However, current …

The problem of selecting a small-size representative summary of a large dataset is a
cornerstone of machine learning, optimization and data science. Motivated by applications …

It is of great importance to design streaming algorithms for submodular maximization, as
many applications (eg, crowdsourcing) have large volume of data satisfying the well …

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 …

In this paper, we consider the problem of maximizing a monotone submodular function
subject to a knapsack constraint in a streaming setting. In such a setting, elements arrive …

Maximizing a monotone submodular function under various constraints is a classical and
intensively studied problem. However, in the single-pass streaming model, where the …

Submodular maximization algorithms have found wide applications in various fields such as
data summarization, recommendation systems, and active learning. In recent years, deletion …