Description
volesti offers advanced algorithms for sampling and volume computation of convex polytopes, but using real-world benchmark datasets requires manual preprocessing. Standard instances like Netlib linear programming test problems and metabolic-network flux polytopes are not directly available in volesti's inequality formats.
This project builds a compact import layer to convert these datasets into volesti-ready H-polytopes with validation, metadata, and examples.
Objectives
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Standard LP benchmarks → volesti H-polytopes: Convert Netlib LP test problems into explicit linear inequalities (matrix (A), vector (b), bounds) that volesti can load directly.
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Metabolic flux polytopes → volesti H-polytopes
- Create reproducible export path from Dingo metabolic-network models to volesti inequality format.
- Document "model → polytope → sampling" workflow for reproducible experiments.
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Validation, tests, benchmark
- Add sanity checks (dimensions, constraint counts, structural validation).
- Curate small benchmark collection (Netlib + metabolic examples) with regeneration scripts.
- Include end-to-end tests verifying converters produce valid volesti input.
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Documentation
- Write tutorials for conversion workflows and minimal sampling/volume pipelines.
Expected Outcomes
- Converters for LP benchmarks and metabolic polytopes to volesti H-polytopes.
- Benchmark examples with reproducible conversion recipes.
- Documentation lowering barrier to volesti benchmarking.
Difficulty: Medium
Size: Medium (175 hours)
Skills Required
- C++
- Linear programing, linear algebra
Mentors
- Vissarion Fisikopoulos <vissarion.fisikopoulos at gmail.com> is an expert in mathematical software, computational geometry, and optimization, and has previous GSOC mentoring experience with Boost C++ libraries (2016-2017) and the R-project (2017).
- Elias Tsigaridas <elias.tsigaridas at inria.fr> is an expert in computational nonlinear algebra and geometry with experience in mathematical software. He has contributed to the implementation, in C and C++, of several solving algorithms for various open source computer algebra libraries and has previous GSOC mentoring experience with the R-project (2019) and Geomscale (2020).