We consider the problem of maximizing a (non-monotone) submodular function subject to a
cardinality constraint. In addition to capturing well-known combinatorial optimization …

Over the last twenty years, an exciting interplay has emerged between proof systems and
algorithms. Some natural families of algorithms can be viewed as a generic translation from …

Submodular maximization under various constraints is a fundamental problem studied
continuously, in both computer science and operations research, since the late 1970's. A …

The study of combinatorial optimization problems with submodular objectives has attracted
much attention in recent years. Such problems are important in both theory and practice …

We initiate a comprehensive experimental study of objective-based hierarchical clustering
methods on massive datasets consisting of deep embedding vectors from computer vision …

In Maximum $ k $-Vertex Cover (Max $ k $-VC), the input is an edge-weighted graph $ G $
and an integer $ k $, and the goal is to find a subset $ S $ of $ k $ vertices that maximizes …

Despite much recent work, the true promise and limitations of the Quantum Alternating
Operator Ansatz (QAOA)[30] are unclear. A critical question regarding QAOA is to what …

Fast algorithms for submodular maximization problems have a vast potential use in
applicative settings, such as machine learning, social networks, and economics. Though fast …

Hierarchical Clustering trees have been widely accepted as a useful form of clustering data,
resulting in a prevalence of adopting fields including phylogenetics, image analysis …

We consider the family of Correlation Clustering optimization problems under fairness
constraints. In Correlation Clustering we are given a graph whose every edge is labeled …