推荐系统入门教程，在线阅读地址：https://datawhalechina.github.io/fun-rec/

KaHyPar (Karlsruhe Hypergraph Partitioning) is a multilevel hypergraph partitioning framework providing direct k-way and recursive bisection based partitioning algorithms that compute solutions of very high quality.

KaHIP -- HIGH Quality Partitioning.

Mt-KaHyPar (Multi-Threaded Karlsruhe Hypergraph Partitioner) is a shared-memory multilevel graph and hypergraph partitioner equipped with parallel implementations of techniques used in the best sequential partitioning algorithms. Mt-KaHyPar can partition extremely large hypergraphs very fast and with high quality.

Agentic Algorithm Engineering

Karlsruhe Rapid Ride-Pooling (KaRRi) Dynamic Ride-Pooling Dispatcher.

Exercises for the Algorithm Engineering (ALE) course at University of Pisa

A list of all publications related to the KaHyPar frameworks.

Dynamic Matching in Practice — fully dynamic maximal matching algorithms

Buffered Streaming Graph Partitioning

Experiment execution and result management for empirical evaluations of algorithms in Python.

Material for the AlgLab (Winter 2024/2025) @ TU Braunschweig

StreamCPI is a framework for reducing the memory consumption of streaming graph partitioners by compressing the array of block assignments used by such partitioners with run-length compression.

A scalable Python framework that transforms algorithm practice into a data-driven, testable, and high-performance workflow—built to help developers grow faster and understand algorithms more deeply.

Fully dynamic exact and heuristic algorithms for edge orientation (delta-orientation)

Exact minimum cuts in hypergraphs at scale using FPT kernelization

Fully dynamic solver for the Maximum (Weight) Independent Set problem

Collection of our hypergraph partitioning experiments

Streaming Process Mapping

Secretly a Master's Thesis