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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.
Karlsruhe Rapid Ridesharing (KaRRi) Dynamic Taxi Sharing Dispatcher.
Exercises for the Algorithm Engineering (ALE) course at University of Pisa
A list of all publications related to the KaHyPar frameworks.
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 workflowβbuilt 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
Automated rooftop solar panel placement system combining computer vision, grid-based modeling, and search/optimization algorithms.
π€ Deploy a simplified voice synthesis service with Fun-CosyVoice3-0.5B-2512, featuring real-time audio output and advanced performance optimizations.
Repo for Algorithm Engineering Projects Team: Suffering IRL
A buffered streaming graph partitioner using prioritized buffering and multilevel refinement. Robust against adversarial node orderings.
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