Submodular Maximization

Submodular maximization focuses on efficiently finding the optimal subset of items that maximizes a submodular function—a function exhibiting diminishing returns. Current research emphasizes developing efficient algorithms, including greedy approaches and those leveraging neural networks (like Deep Submodular Functions and their extensions), to tackle various constraints (e.g., cardinality, matroid, knapsack) and handle both monotone and non-monotone functions. These advancements are crucial for diverse applications such as active learning, resource allocation, and data summarization, improving the efficiency and scalability of solutions in these fields. Furthermore, research is actively exploring decentralized and federated settings to address the challenges of large-scale and distributed data.