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 -- Karlsruhe 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.
Exercises for the Algorithm Engineering (ALE) course at University of Pisa
A list of all publications related to the KaHyPar frameworks.
Karlsruhe Rapid Ridesharing (KaRRi) Dynamic Taxi Sharing Dispatcher.
Material for the AlgLab (Winter 2024/2025) @ TU Braunschweig
Experiment execution and result management for empirical evaluations of algorithms in Python.
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.
Collection of our hypergraph partitioning experiments
Secretly a Master's Thesis
Graph Coloring implementation based on XRLF algorithm
Repo for Algorithm Engineering Projects Team: Suffering IRL
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