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Sampling with Riemannian Hamiltonian Monte Carlo
Implement sampling with Riemannian Hamiltonian Monte Carlo (RiHMC). The method is described in [1] and there is MATLAB code available on github.
References:
[1] Kook et al - Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space https://arxiv.org/pdf/2202.01908.pdf
The goal of this project is to make RiHMC available in volesti. Current implementation of RiHMC includes MATLAB routines as well as C++ code.
Difficulty: Hard
- Required: C++, MATLAB, Probability theory, Basic applied math background
- Preferred: Experience with mathematical software is a plus
The projects will provide GeomScale with a new practical high-dimensional sampler.
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Apostolos Chalkis <tolis.chal at gmail.com> is an expert in statistical software, computational geometry, and optimization, and has previous GSoC student experience (2018 & 2019) and mentoring experience with GeomScale (2020 & 2021).
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Vissarion Fisikopoulos <vissarion.fisikopoulos at gmail.com> is an international 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).
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Marios Papachristou < papachristoumarios at gmail.com > is a PhD student in the Computer Science Department at Cornell University. His primary research interests lie within the field of Data Science. He has previous experience in GSoC 2018 and 2020 as a student under Org. FOSS and GeomScale. He was GSoC mentor in GSoC 2019.
Students, please contact the mentors after completing at least one of the tests below.
Students, please do one or more of the following tests before contacting the mentors.
- Easy: compile and run
volesti. Use the R extension to visualize sampling (with any sampling algorithm) in a polytope. Compile and build https://github.com/ConstrainedSampler/PolytopeSamplerMatlab and run same examples. - Medium: Reproduce as many as possible from the experiments in https://arxiv.org/pdf/2202.01908.pdf
- Hard: Use a more efficient than CDHR sampling algorithm implemented in volesti and do some experiments with the dataset in https://arxiv.org/pdf/2202.01908.pdf
Students, please post a link to your test results here.
- EXAMPLE STUDENT 1 NAME, LINK TO GITHUB PROFILE, LINK TO TEST RESULTS.
STUDENT 1 AGILAN S, https://github.com/Agi7an, https://github.com/Agi7an/VolEsti/blob/main/setup.r
STUDENT 2 Ioannis Iakovidis, https://github.com/iakoviid/, https://github.com/iakoviid/volesti/tree/gsoc2022
STUDENT 3 HUSSAIN LOHAWALA, https://github.com/H9660/Volesti-/blob/main/setup.r
STUDENT 4 Huu Phuoc Le, https://github.com/huuphuocle, https://github.com/huuphuocle/sampling_correlation_matrices