The main development is currently performed in volesti, a generic open source C++ library, with R and Python interfaces. volesti implements various algorithms for high-dimensional sampling and volume approximation as well as functions for copula estimation in financial modelling and metabolic network analysis.
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dingo is a python package that analyzes metabolic networks. It relies on high dimensional sampling with Markov Chain Monte Carlo (MCMC) methods and fast optimization methods to analyze the possible states of a metabolic network. To perform MCMC sampling, dingo relies on the C++ library volesti, which provides several algorithms for sampling convex polytopes. dingo also performs two standard methods to analyze the flux space of a metabolic network, namely Flux Balance Analysis and Flux Variability Analysis.
Who is using our software ?
G. Spallitta, G. Masina, P. Morettin, A. Passerini, R. Sebastiani, Enhancing SMT-based Weighted Model Integration by Structure Awareness, Preprint, 2023. WWW
M. Vejdemo-Johansson, S. Mukherjee, Multiple hypothesis testing with persistent homology, American Institute of Mathematical Sciences, Volume 4, 2022. WWW
A. Venzke, D.K. Molzahn, S. Chatzivasileiadis - Efficient creation of datasets for data-driven power system applications, Electric Power Systems Research, Volume 190, 2021. WWW
P.Z.D. Martires, Samuel Kolb - Monte Carlo Anti-Differentiation for Approximate Weighted Model Integration, 2020. WWW
C. Maria, O. Rouillé - Computation of Large Asymptotics of 3-Manifold Quantum Invariants, 2020. WWW
Google Summer of Code
GeomScale is participating in Google Summer of Code 2023 as a mentoring organization!
GeomScale was accepted and participated in Google Summer of Code 2020, Google Summer of Code 2021 and Google Summer of Code 2022 as a mentoring organization. The following projects was successfully completed:
- Memory allocation in facet redundancy removal in dingo (GSoC 2022).
- Sampling correlation matrices (GSoC 2022).
- Randomized SDP solver with Riemannian Hamiltonian Monte Carlo (GSoC 2022).
- Support for new sampling methods and new model formats in dingo (GSoC 2022).
- Counting linear extensions with volume computation (GSoC 2022).
Automatic differentiation support in volesti (GSoC 2022).
- From DNA sequences to metabolic interactions: building a pipeline to extract key metabolic processes (GSoC 2021).
- Monte Carlo Integration (GSoC 2021).
- Counting linear extensions (GSoC 2021).
- High dimensional geometric computations with least matrix inequalities (GSoC 2021).
Parallel Geometric Random Walks with Sparse Numerical Optimizations (GSoC 2021).
- A comparative study of uniform high dimensional samplers (GSoC 2020).
Sampling from High-Dimensional log-concave densities (GSoC 2020).
- Optimization and Sum of Squares (GSoC 2020).
Members of GeomScale has successfully participated in GSoC in the past with the R-project for statistical computing.