We propose subsampling as a unified algorithmic technique for submodular maximization in
centralized and online settings. The idea is simple: independently sample elements from the …

Most existing submodular maximization algorithms provide theoretical guarantees with
approximation bounds. However, in many cases, users may be interested in an anytime …

In this paper, we consider an online optimization process, where the objective functions are
not convex (nor concave) but instead belong to a broad class of continuous submodular …

We study the problem of maximizing a nonmonotone submodular function subject to a
cardinality constraint in the streaming model. Our main contribution is a single-pass (semi) …

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

We present a simple combinatorial 1− e− 2 2-approximation algorithm for maximizing a
monotone submodular function subject to a knapsack and a matroid constraint. This classic …

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

Abstract In the Cardinality-Constrained Maximization (Minimization) problem the input is a
universe 𝒰, a function f: 2^{{𝒰}}→ ℝ, and an integer k, and the task is to find a set S⊆ 𝒰 with …

We study the non-submodular maximization problem, in which the objective function is
characterized by parameters, subject to a cardinality or p-system constraint. By adapting the …

Optimization of DR-submodular functions has experienced a notable surge in significance in
recent times, marking a pivotal development within the domain of non-convex optimization …