apricot implements submodular optimization for the purpose of selecting subsets of massive data sets to train machine learning models quickly. See the documentation page: https://apricot-select.readthedocs.io/en/latest/index.html

Summarize Massive Datasets using Submodular Optimization

Submodular optimization for context engineering: query fan-out, text selection, passage reranking

visual-inertial odometry (VINS-Mono) with motion-aware feature selection

NAACL 2019: Submodular optimization-based diverse paraphrasing and its effectiveness in data augmentation

A collection of optimization algorithms for maximizing unconstrained submodular set functions.

(IJCAI 2019) Submodular Batch Selection for Training Deep Neural Networks

Code for the paper: Combining Graph Degeneracy and Submodularity for Unsupervised Extractive Summarization

PyEDCR is a metacognitive neuro-symbolic method for learning error detection and correction rules in deployed ML models using combinatorial sub-modular set optimization

MUSS: Multilevel Subset Selection for relevance and diversity at scale (UAI 2026) — up to 80x faster than MMR with approximation guarantees, for RAG and candidate retrieval

Efficient greedy-based methods for constrained submodular optimization.

Memory Efficient Max Flow

Greedy and Lazy Greedy Sub Modular Optimisation

Recommending movies based on a utility function of movie ratings

Some tools for submodular function minimization in Python

Probabilistic Gas Leak Rate Estimation using Submodular Function Maximization with Routing Constraints

Code for our ICML '24 paper, "Submodular framework for structured-sparse optimal transport".

A GPU accelerated Submodular Optimization toolkit.

A Survey on Optimization of Submodular Functions (GATech Course Project: Intro to Grad Studies CS7001, Spring 2014)

InSQuaD is a research framework for efficient in-context learning that leverages submodular mutual information to optimize the quality-diversity tradeoff in example selection for large language models