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Build dingo reference
GeomScale's software package volesti
provides efficient multidimensional sampling and volume computations in high dimensions. These computational tools are crucial for multiply purposes, including modern machine learning and data science. For example, using sampling, we can solve efficiently problems in optimization, while volume computation, a fundamental and computationally hard problem, is a cornerstone for efficient algorithms for integration. Moreover, volesti
provides functionality for high-dimensional copula estimation that is useful, among other things, to model financial crises. volesti
is written in C++ but there are also interfaces in R and python.
An introduction to volesti
is of great interest as it concerns several diverged scientific and business communities. This documentation should explain how volesti
can be used (a) to handle abstract geometric notions in practice, (b) to perform hard computations giving ordinary as well as extreme examples, and (c) to solve difficult problems presented in scientific and business applications.
In this project we want to focus on building a reference for the dingo
python library to document all functions, classes etc.
The scipy project https://www.scipy.org/docs.html
The project could be split in the following tasks:
- Choose the documentation system e.g. doxygen / Sphinx(used by https://readthedocs.org) etc
- Document the python interface of
dingo
- Get feedback, test, improve the documentation
This is an important project to communicate the usability and computational power that dingo
could provide to biology and bioinformatics communities.
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Haris Zafeiropoulos <haris.zafr at gmail.com> is a PhD candidate in microbial ecology and bioinformatics. His research focuses on organisms - environments - metabolic processes associations and microbial interactions by taking advantage of computational methods. He has been a contributor to the
volesti
project since 2020. -
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 (2016-2019) and the R-project (2017-2019).
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Apostolos Chalkis <tolis.chal at gmail.com> is a PhD student in Computer Science. His research focuses on mathematical computing, optimization and computational finance. He has previous experience in GSoC 2018 and 2019 as a student under Org.
R-project
, implementing state-of-the-art algorithms for sampling from high dimensional multivariate distributions. He is one of the authors ofvolesti
.